Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease
Purpose: White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and ad...
Saved in:
| Published in: | Medical physics (Lancaster) Vol. 43; no. 12; pp. 6246 - 6258 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
American Association of Physicists in Medicine
01.12.2016
|
| Subjects: | |
| ISSN: | 0094-2405, 2473-4209, 2473-4209 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Purpose:
White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small.
Methods:
A two-stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in-plane effective diameter). For each size-specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first-stage classifiers were combined into a single WMH likelihood by a second-stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free-response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs.
Results:
Experimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives.
Conclusions:
The authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader. |
|---|---|
| AbstractList | Purpose:
White matter hyperintensities (WMH) are seen on FLAIR‐MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small.
Methods:
A two‐stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in‐plane effective diameter). For each size‐specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first‐stage classifiers were combined into a single WMH likelihood by a second‐stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free‐response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs.
Results:
Experimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives.
Conclusions:
The authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader. White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small. A two-stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in-plane effective diameter). For each size-specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first-stage classifiers were combined into a single WMH likelihood by a second-stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free-response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs. Experimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives. The authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader. White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small.PURPOSEWhite matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small.A two-stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in-plane effective diameter). For each size-specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first-stage classifiers were combined into a single WMH likelihood by a second-stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free-response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs.METHODSA two-stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in-plane effective diameter). For each size-specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first-stage classifiers were combined into a single WMH likelihood by a second-stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free-response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs.Experimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives.RESULTSExperimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives.The authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader.CONCLUSIONSThe authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader. |
| Author | Ghafoorian, Mohsen van Uden, Inge W. M. Platel, Bram de Leeuw, Frank-Erik Karssemeijer, Nico Heskes, Tom Marchiori, Elena |
| Author_xml | – sequence: 1 givenname: Mohsen surname: Ghafoorian fullname: Ghafoorian, Mohsen email: mohsen.ghafoorian@radboudumc.nl organization: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525, The Netherlands and Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 GA, The Netherlands – sequence: 2 givenname: Nico surname: Karssemeijer fullname: Karssemeijer, Nico organization: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525, The Netherlands – sequence: 3 givenname: Inge W. M. surname: van Uden fullname: van Uden, Inge W. M. organization: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands – sequence: 4 givenname: Frank-Erik surname: de Leeuw fullname: de Leeuw, Frank-Erik organization: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands – sequence: 5 givenname: Tom surname: Heskes fullname: Heskes, Tom organization: Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, The Netherlands – sequence: 6 givenname: Elena surname: Marchiori fullname: Marchiori, Elena organization: Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, The Netherlands – sequence: 7 givenname: Bram surname: Platel fullname: Platel, Bram organization: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525, The Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27908171$$D View this record in MEDLINE/PubMed |
| BookMark | eNp90E1rGzEQBmBRHBo77aF_oOyxDWwyo5VXq2MwaRNISA7tWWilWaKyH64kxzi_vmtsQwlxTkKjZ17EO2OTfuiJsS8IF4hYXeKFUGUJXH1gUy5kkQsOasKmAErkXMD8lM1i_AMAZTGHj-yUSwUVSpyy-mqVhs4kcpmjRDb5oc-GJls_-UTZ-JAoZE-bJQXfJ-qjT57iFpi2zaJ_GS--zywFqoMZJ912_kwxUps5H8lE-sROGtNG-rw_z9jvH9e_Fjf53cPP28XVXW4Fr1RezUtZFM4WDhsJjbKuRCOdEyBRgjICFccapW0aPgIlSguEWI60Bm6xOGPfdrnLMPxdUUy689FS25qehlXUWIl5xSWHaqRf93RVd-T0MvjOhI0-9DKCyx2wYYgxUKOtT2ZbTgrGtxpBb5vXqPfNjxvfX20cQt-y-c6ufUub41DfP-79-c7Hwy_eDT-Kn4fwX_jSNcU_TymsBQ |
| CODEN | MPHYA6 |
| CitedBy_id | crossref_primary_10_1002_hbm_25273 crossref_primary_10_1016_j_media_2021_102215 crossref_primary_10_1109_TMI_2017_2693978 crossref_primary_10_3389_fneur_2025_1628787 crossref_primary_10_1161_STROKEAHA_121_038099 crossref_primary_10_1016_j_nicl_2021_102667 crossref_primary_10_1002_hipo_23039 crossref_primary_10_1212_WNL_0000000000007095 crossref_primary_10_1161_STROKEAHA_122_039903 crossref_primary_10_1016_j_nicl_2017_12_007 crossref_primary_10_1097_CCM_0000000000005313 crossref_primary_10_1016_j_nicl_2017_10_007 crossref_primary_10_1038_s41598_019_52966_0 crossref_primary_10_1016_j_nicl_2018_02_033 crossref_primary_10_1212_WNL_0000000000004490 crossref_primary_10_1109_TMI_2019_2905770 crossref_primary_10_1007_s11357_024_01238_5 crossref_primary_10_1177_23969873221100331 crossref_primary_10_1176_appi_ajp_20220380 crossref_primary_10_1136_jnnp_2019_321767 crossref_primary_10_1016_j_media_2021_102184 crossref_primary_10_1016_j_cccb_2025_100396 crossref_primary_10_1212_WNL_0000000000008364 crossref_primary_10_1212_WNL_0000000000008002 crossref_primary_10_1161_STROKEAHA_117_019810 crossref_primary_10_3233_JAD_200496 crossref_primary_10_1038_s41598_017_05300_5 crossref_primary_10_1007_s11357_024_01306_w crossref_primary_10_1016_j_parkreldis_2018_11_010 crossref_primary_10_1016_j_cmpb_2018_04_011 crossref_primary_10_1016_j_jocn_2019_06_038 crossref_primary_10_1212_WNL_0000000000209306 crossref_primary_10_1212_WNL_0000000000209701 crossref_primary_10_1212_WNL_0000000000209148 crossref_primary_10_1186_s12883_024_04010_6 crossref_primary_10_3390_app15052830 crossref_primary_10_1016_j_neuroimage_2017_06_009 crossref_primary_10_3390_app11041675 crossref_primary_10_1136_bmjopen_2020_042660 crossref_primary_10_1002_ana_26615 crossref_primary_10_1016_j_nicl_2022_102955 crossref_primary_10_1016_j_nicl_2019_102048 crossref_primary_10_1038_s41598_021_87013_4 crossref_primary_10_1177_1747493020984090 crossref_primary_10_1136_jnnp_2021_326571 crossref_primary_10_1016_j_compmedimag_2018_05_001 crossref_primary_10_1161_STROKEAHA_118_020980 crossref_primary_10_1161_STROKEAHA_118_021993 crossref_primary_10_1136_jnnp_2022_330091 |
| Cites_doi | 10.1016/j.neuroimage.2009.09.005 10.1038/s41598-017-05300-5 10.1117/12.2007940 10.1006/jcss.1997.1504 10.1136/jnnp.70.1.9 10.1109/EMBC.2012.6347208 10.1097/00004728‐197906000‐00001 10.1212/01.WNL.0000132635.75819.E5 10.1016/j.biopsych.2008.03.024 10.1212/WNL.57.6.990 10.1109/42.938237 10.1016/j.neuroimage.2005.06.061 10.1016/j.nicl.2015.05.003 10.1016/j.neuroimage.2011.04.053 10.1016/j.compbiomed.2007.12.005 10.1002/mds.870130119 10.1056/NEJMoa022066 10.1007/s12021‐015‐9260‐y 10.1016/j.neuroimage.2003.10.012 10.1016/j.jagp.2014.07.002 10.1109/TBME.2011.2181167 10.1371/journal.pone.0104011 10.1161/STROKEAHA.113.000947 10.1136/jamia.2001.0080401 10.1109/TMI.2014.2382561 10.1016/S1474‐4422(13)70124‐8 10.1016/j.imavis.2008.09.003 10.1023/A:1010933404324 10.1007/978-3-642-15705-9_14 10.1002/hbm.22472 10.1159/000081050 10.1212/WNL.54.4.838 10.1212/01.wnl.0000305959.46197.e6 10.1016/j.jneumeth.2012.12.014 10.1016/j.media.2012.09.004 10.1002/hbm.10062 10.1111/j.1600‐0404.2005.00553.x 10.1186/1471‐2377‐11‐29 10.1016/j.neuroimage.2009.01.011 10.1109/TMI.2012.2186639 10.1117/12.2081597 10.1002/1531‐8249(200002)47:2<145::AID‐ANA3>3.0.CO;2‐P 10.1016/j.neuroimage.2011.11.032 10.1161/01.STR.31.9.2182 10.1117/12.955926 10.1002/hbm.22332 10.1016/S1361‐8415(01)00036‐6 10.1136/jnnp.2007.124651 10.1016/j.nicl.2013.10.003 10.1109/42.906424 10.1109/NNSP.1991.239541 10.1109/ISBI.2016.7493532 10.1214/aos/1016218223 |
| ContentType | Journal Article |
| Copyright | American Association of Physicists in Medicine 2016 American Association of Physicists in Medicine |
| Copyright_xml | – notice: American Association of Physicists in Medicine – notice: 2016 American Association of Physicists in Medicine |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1118/1.4966029 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Physics |
| EISSN | 2473-4209 |
| EndPage | 6258 |
| ExternalDocumentID | 27908171 10_1118_1_4966029 MP6029 |
| Genre | article Journal Article |
| GroupedDBID | --- --Z -DZ .GJ 0R~ 1OB 1OC 29M 2WC 33P 36B 3O- 4.4 476 53G 5GY 5RE 5VS AAHHS AANLZ AAQQT AASGY AAXRX AAZKR ABCUV ABEFU ABFTF ABJNI ABLJU ABQWH ABTAH ABXGK ACAHQ ACBEA ACCFJ ACCZN ACGFO ACGFS ACGOF ACPOU ACSMX ACXBN ACXQS ADBBV ADBTR ADKYN ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AENEX AEQDE AEUYR AFBPY AFFPM AHBTC AIACR AIAGR AIURR AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN AMYDB ASPBG BFHJK C45 CS3 DCZOG DRFUL DRMAN DRSTM DU5 EBD EBS EJD EMB EMOBN F5P G8K HDBZQ HGLYW I-F KBYEO LATKE LEEKS LOXES LUTES LYRES MEWTI O9- OVD P2P P2W PALCI PHY RJQFR RNS ROL SAMSI SUPJJ SV3 TEORI TN5 TWZ USG WOHZO WXSBR XJT ZGI ZVN ZXP ZY4 ZZTAW AAHQN AAIPD AAMNL AAYCA ABDPE AFWVQ AITYG ALVPJ AAMMB AAYXX ABUFD ADMLS AEFGJ AEYWJ AGHNM AGXDD AGYGG AIDQK AIDYY AIQQE CITATION LH4 CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c4289-856733dc3d1f70f9cd61a7dd4071709a41921b17cff2f70946c0e1160f9b02c13 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 63 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000390237200005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0094-2405 2473-4209 |
| IngestDate | Sun Nov 09 11:57:14 EST 2025 Wed Feb 19 01:57:18 EST 2025 Sat Nov 29 06:02:28 EST 2025 Tue Nov 18 21:26:09 EST 2025 Wed Jan 22 16:30:48 EST 2025 Fri Jun 21 00:14:44 EDT 2024 Sun Jul 14 10:28:27 EDT 2019 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Keywords | Small vessel disease automated detection adaboost computer aided detection white matter hyperintensity |
| Language | English |
| License | 0094-2405/2016/43(12)/6246/13/$30.00 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4289-856733dc3d1f70f9cd61a7dd4071709a41921b17cff2f70946c0e1160f9b02c13 |
| Notes | mohsen.ghafoorian@radboudumc.nl Electronic mail ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1118/1.4966029 |
| PMID | 27908171 |
| PQID | 1845827208 |
| PQPubID | 23479 |
| PageCount | 13 |
| ParticipantIDs | crossref_citationtrail_10_1118_1_4966029 crossref_primary_10_1118_1_4966029 scitation_primary_10_1118_1_4966029 pubmed_primary_27908171 wiley_primary_10_1118_1_4966029_MP6029 proquest_miscellaneous_1845827208 |
| PublicationCentury | 2000 |
| PublicationDate | 20161200 December 2016 2016-12-00 2016-Dec 20161201 |
| PublicationDateYYYYMMDD | 2016-12-01 |
| PublicationDate_xml | – month: 12 year: 2016 text: 20161200 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Medical physics (Lancaster) |
| PublicationTitleAlternate | Med Phys |
| PublicationYear | 2016 |
| Publisher | American Association of Physicists in Medicine |
| Publisher_xml | – name: American Association of Physicists in Medicine |
| References | García-Lorenzo, Francis, Narayanan, Arnold, Collins (c37) 2013 Schoonheim, Vigeveno, Lopes, Pouwels, Polman, Barkhof, Geurts (c10) 2014 Baezner, Blahak, Poggesi, Pantoni, Inzitari, Chabriat, Erkinjuntti, Fazekas, Ferro, Langhorne, O’Brien, Scheltens, Visser, Wahlund, Waldemar, Wallin, Hennerici (c3) 2008 Caligiuri, Perrotta, Augimeri, Rocca, Quattrone, Cherubini (c36) 2015 Vermeer, Prins, den Heijer, Hofman, Koudstaal, Breteler (c7) 2003 Freund, Schapire (c52) 1997 van Zagten, Lodder, Kessels (c6) 1998 De Leeuw, de Groot, Achten, Oudkerk, Ramos, Heijboer, Hofman, Jolles, Van Gijn, Breteler (c1) 2001 van Norden, de Laat, Gons, van Uden, van Dijk, van Oudheusden, Esselink, Bloem, van Engelen, Zwarts, Tendolkar, Olde-Rikkert, van der Vlugt, Zwiers, Norris, de Leeuw (c9) 2011 Friedman, Hastie, Tibshirani (c53) 2000 Jenkinson, Smith (c43) 2001 Weinstein, Beiser, DeCarli, Au, Wolf, Seshadri (c13) 2013 Shi, Wang, Liu, Pu, Wang, Chu, Ahuja, Wang (c20) 2013 Smith, Snowdon, Wang, Markesbery (c12) 2000 Whitman, Tang, Lin, Baloh (c15) 2001 Tsai, Peng, Chen, Wang, Li, Wang, Chen, Lin, Smith, Wu, Sung, Yeh, Hsin (c26) 2014 Zhang, Brady, Smith (c46) 2001 de Groot, Oudkerk, Gijn, Hofman, Jolles, Breteler (c4) 2000 Klöppel, Abdulkadir, Hadjidemetriou, Issleib, Frings, Thanh, Mader, Teipel, Hüll, Ronneberger (c27) 2011 Mazziotta, Toga, Evans, Fox, Lancaster, Zilles, Woods, Paus, Simpson, Pike, Holmes, Collins, Thompson, MacDonald, Iacoboni, Schormann, Amunts, Palomero-Gallagher, Geyer, Parsons, Narr, Kabani, Le Goualher, Feidler, Smith, Boomsma, Pol, Cannon, Kawashima, Mazoyer (c44) 2001 Wardlaw, Smith, Biessels, Cordonnier, Fazekas, Frayne, Lindley, O’Brien, Barkhof, Benavente, Black, Brayne, Breteler, Chabriat, Decarli, de Leeuw, Doubal, Duering, Fox, Greenberg, Hachinski, Kilimann, Mok, Oostenbrugge, Pantoni, Speck, Stephan, Teipel, Viswanathan, Werring, Chen, Smith, van Buchem, Norrving, Gorelick, Dichgans (c2) 2013 de Boer, Vrooman, van der Lijn, Vernooij, Ikram, van der Lugt, Breteler, Niessen (c24) 2009 Khayati, Vafadust, Towhidkhah, Nabavi (c23) 2008 Ghafoorian, Karssemeijer, van Uden, de Leeuw, Heskes, Marchiori, Platel (c48) 2015 Karimaghaloo, Shah, Francis, Arnold, Collins, Arbel (c31) 2012 Hirono, Kitagaki, Kazui, Hashimoto, Mori (c11) 2000 Steenwijk, Pouwels, Daams, van Dalen, Caan, Richard, Barkhof, Vrenken (c34) 2013 Kuijper (c50) 2009 Smith (c45) 2002 Marshall, Shchelchkov, Kaufer, Ivanco, Bohnen (c14) 2006 Van Leemput, Maes, Vandermeulen, Colchester, Suetens (c22) 2001 Hoffman, Huang, Phelps (c39) 1979 Schmidt, Scheltens, Erkinjuntti, Pantoni, Markus, Wallin, Barkhof, Fazekas (c38) 2004 Ithapu, Singh, Lindner, Austin, Hinrichs, Carlsson, Bendlin, Johnson (c28) 2014 Shiee, Bazin, Ozturk, Reich, Calabresi, Pham (c21) 2010 Pantoni, Basile, Pracucci, Asplund, Bogousslavsky, Chabriat, Erkinjuntti, Fazekas, Ferro, Hennerici, O’Brien, Scheltens, Visser, Wahlund, Waldemar, Wallin, Inzitari (c8) 2004 Herrmann, Le Masurier, Ebmeier (c16) 2008 Schmidt, Gaser, Arsic, Buck, Förschler, Berthele, Hoshi, Ilg, Schmid, Zimmer, Hemmer, Mhlau (c25) 2012 Karimaghaloo, Rivaz, Arnold, Collins, Arbel (c35) 2015 Zijdenbos, Dawant (c30) 1993 Kim, MacFall, Payne (c42) 2008 Admiraal-Behloul, Van Den Heuvel, Olofsen, van Osch, van der Grond, Van Buchem, Reiber (c17) 2005 Jain, Sima, Ribbens, Cambron, Maertens, Van Hecke, De Mey, Barkhof, Steenwijk, Daams, Maes, Van Huffel, Vrenken, Smeets (c18) 2015 Anbeek, Vincken, van Osch, Bisschops, van der Grond (c33) 2004 Riad, Platel, de Leeuw, Karssemeijer (c29) 2013 Breiman (c51) 2001 van Uden, Tuladhar, de Laat, van Norden, Norris, van Dijk, Tendolkar, de Leeuw (c5) 2015 Khademi, Venetsanopoulos, Moody (c19) 2012 Bunch, Hamilton, Sanderson, Simmons (c41) 1977 2009; 45 2004; 21 2015; 34 2002; 17 2004; 63 2013; 3 2000; 47 1993; 22 2008; 38 2004; 24 2011; 11 2008; 79 2011; 57 2012; 59 2001; 45 2008; 70 2005; 28 2013; 17 1997; 55 2013; 12 2000; 54 1979; 3 2008; 64 2014; 9 2001; 57 1998; 13 1988 2015; 13 2001; 70 2000; 28 2013; 44 2012 2010 2013; 8670 1991 2015; 9414 2015; 8 2009; 27 2012; 31 2001; 20 2006; 113 2015; 23 2010; 49 2003; 348 2001; 5 2000; 31 2001; 8 2013; 213 2014; 35 2016 1977; 0127 e_1_2_8_28_1 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 McLachlan G. J. (e_1_2_8_48_1) 1988 e_1_2_8_32_1 e_1_2_8_55_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 e_1_2_8_51_1 e_1_2_8_30_1 e_1_2_8_29_1 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_23_1 e_1_2_8_44_1 Zijdenbos A. P. (e_1_2_8_31_1) 1993; 22 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_10_1 e_1_2_8_56_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_52_1 e_1_2_8_50_1 |
| References_xml | – start-page: 145 year: 2000 ident: c4 article-title: Cerebral white matter lesions and cognitive function: The rotterdam scan study publication-title: Ann. Neurol. – start-page: 138 year: 2013 ident: c20 article-title: Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction publication-title: J. Neurosci. Methods – start-page: 4219 year: 2014 ident: c28 article-title: Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies publication-title: Hum. Brain Mapp. – start-page: 416 year: 2011 ident: c27 article-title: A comparison of different automated methods for the detection of white matter lesions in MRI data publication-title: NeuroImage – start-page: 1215 year: 2003 ident: c7 article-title: Silent brain infarcts and the risk of dementia and cognitive decline publication-title: N. Engl. J. Med. – start-page: 867014 year: 2013 ident: c29 article-title: Detection of white matter lesions in cerebral small vessel disease publication-title: Proc. SPIE – start-page: 9 year: 2001 ident: c1 article-title: Prevalence of cerebral white matter lesions in elderly people: A population based magnetic resonance imaging study. The rotterdam scan study publication-title: J. Neurol., Neurosurg. Psychiatry – start-page: 119 year: 1997 ident: c52 article-title: A decision-theoretic generalization of on-line learning and an application to boosting publication-title: J. Comput. Syst. Sci. – start-page: 677 year: 2001 ident: c22 article-title: Automated segmentation of multiple sclerosis lesions by model outlier detection publication-title: IEEE Trans. Med. Imaging – start-page: 1524 year: 2010 ident: c21 article-title: A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions publication-title: NeuroImage – start-page: 299 year: 1979 ident: c39 article-title: Quantitation in positron emission computed tomography: 1. Effect of object size publication-title: J. Comput. Assisted Tomogr. – start-page: 401 year: 2001 ident: c44 article-title: A four-dimensional probabilistic atlas of the human brain publication-title: J. Am. Med. Inf. Associat. – start-page: 990 year: 2001 ident: c15 article-title: A prospective study of cerebral white matter abnormalities in older people with gait dysfunction publication-title: Neurology – start-page: 124 year: 1977 ident: c41 article-title: A free response approach to the measurement and characterization of radiographic observer performance publication-title: Proc. SPIE – start-page: 935 year: 2008 ident: c3 article-title: Association of gait and balance disorders with age-related white matter changes the ladis study publication-title: Neurology – start-page: 941411 year: 2015 ident: c48 article-title: Small white matter lesion detection in cerebral small vessel disease publication-title: Proc. SPIE – start-page: 1181 year: 2012 ident: c31 article-title: Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields publication-title: IEEE Trans. Med. Imaging – start-page: 87 year: 2006 ident: c14 article-title: White matter hyperintensities and cortical acetylcholinesterase activity in parkinsonian dementia publication-title: Acta Neurol. Scand. – start-page: 860 year: 2012 ident: c19 article-title: Robust white matter lesion segmentation in flair MRI publication-title: IEEE Trans. Biomed. Eng. – start-page: 367 year: 2015 ident: c18 article-title: Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images publication-title: NeuroImage: Clin. – start-page: 1 year: 2013 ident: c37 article-title: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging publication-title: Med. Image Anal. – start-page: 607 year: 2005 ident: c17 article-title: Fully automatic segmentation of white matter hyperintensities in MR images of the elderly publication-title: NeuroImage – start-page: 822 year: 2013 ident: c2 article-title: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration publication-title: Lancet Neurol. – start-page: 379 year: 2008 ident: c23 article-title: Fully automatic segmentation of multiple sclerosis lesions in brain MR flair images using adaptive mixtures method and markov random field model publication-title: Comput. Biol. Med. – start-page: 89 year: 1998 ident: c6 article-title: Gait disorder and parkinsonian signs in patients with stroke related to small deep infarcts and white matter lesions publication-title: Mov. Disord. – start-page: 619 year: 2008 ident: c16 article-title: White matter hyperintensities in late life depression: A systematic review publication-title: J. Neurol., Neurosurg. Psychiatry – start-page: 1037 year: 2004 ident: c33 article-title: Probabilistic segmentation of white matter lesions in MR imaging publication-title: NeuroImage – start-page: 45 year: 2001 ident: c46 article-title: Segmentation of brain MR images through a hidden markov random field model and the expectation–maximization algorithm publication-title: IEEE Trans. Med. Imaging – start-page: 273 year: 2008 ident: c42 article-title: Classification of white matter lesions on magnetic resonance imaging in elderly persons publication-title: Biol. Psychiatry – start-page: 525 year: 2015 ident: c5 article-title: White matter integrity and depressive symptoms in cerebral small vessel disease: The run DMC study publication-title: Am. J. Geriatr. Psychiatry – start-page: 1151 year: 2009 ident: c24 article-title: White matter lesion extension to automatic brain tissue segmentation on MRI publication-title: NeuroImage – start-page: 51 year: 2004 ident: c8 article-title: Impact of age-related cerebral white matter changes on the transition to disability—The LADIS study: Rationale, design and methodology publication-title: Neuroepidemiology – start-page: 2182 year: 2000 ident: c11 article-title: Impact of white matter changes on clinical manifestation of Alzheimer’s disease a quantitative study publication-title: Stroke – start-page: 462 year: 2013 ident: c34 article-title: Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs) publication-title: NeuroImage: Clin. – start-page: 143 year: 2001 ident: c43 article-title: A global optimisation method for robust affine registration of brain images publication-title: Med. Image Anal. – start-page: 2348 year: 2014 ident: c10 article-title: Sex-specific extent and severity of white matter damage in multiple sclerosis: Implications for cognitive decline publication-title: Hum. Brain Mapp. – start-page: 838 year: 2000 ident: c12 article-title: White matter volumes and periventricular white matter hyperintensities in aging and dementia publication-title: Neurology – start-page: 2787 year: 2013 ident: c13 article-title: Brain imaging and cognitive predictors of stroke and Alzheimer disease in the framingham heart study publication-title: Stroke – start-page: 1023 year: 2009 ident: c50 article-title: Geometrical pdes based on second-order derivatives of gauge coordinates in image processing publication-title: Image Vis. Comput. – start-page: 139 year: 2004 ident: c38 article-title: White matter lesion progression a surrogate endpoint for trials in cerebral small-vessel disease publication-title: Neurology – start-page: 401 year: 1993 ident: c30 article-title: Brain segmentation and white matter lesion detection in MR images publication-title: Crit. Rev. Biomed. Eng. – start-page: 1 year: 2015 ident: c36 article-title: Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: A review publication-title: Neuroinformatics – start-page: 1227 year: 2015 ident: c35 article-title: Temporal hierarchical adaptive texture crf for automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI publication-title: IEEE Trans. Med. Imaging – start-page: 29 year: 2011 ident: c9 article-title: Causes and consequences of cerebral small vessel disease. The run DMC study: A prospective cohort study. Study rationale and protocol publication-title: BMC Neurol. – start-page: 3774 year: 2012 ident: c25 article-title: An automated tool for detection of flair-hyperintense white-matter lesions in multiple sclerosis publication-title: NeuroImage – start-page: 143 year: 2002 ident: c45 article-title: Fast robust automated brain extraction publication-title: Hum. Brain Mapp. – start-page: 337 year: 2000 ident: c53 article-title: Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors) publication-title: Ann. Stat. – start-page: e104011 year: 2014 ident: c26 article-title: Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction publication-title: PloS One – start-page: 5 year: 2001 ident: c51 article-title: Random forests publication-title: Mach. Learn. – volume: 3 start-page: 299 issue: 3 year: 1979 end-page: 308 article-title: Quantitation in positron emission computed tomography: 1. Effect of object size publication-title: J. Comput. Assisted Tomogr. – start-page: 1414 year: 2016 end-page: 1417 article-title: Non‐uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation – volume: 8 start-page: 367 year: 2015 end-page: 375 article-title: Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images publication-title: NeuroImage: Clin. – volume: 17 start-page: 143 issue: 3 year: 2002 end-page: 155 article-title: Fast robust automated brain extraction publication-title: Hum. Brain Mapp. – start-page: 5372 year: 2012 end-page: 5375 article-title: Automated segmentation of free‐lying cell nuclei in pap smears for malignancy‐associated change analysis – volume: 57 start-page: 990 issue: 6 year: 2001 end-page: 994 article-title: A prospective study of cerebral white matter abnormalities in older people with gait dysfunction publication-title: Neurology – volume: 28 start-page: 607 issue: 3 year: 2005 end-page: 617 article-title: Fully automatic segmentation of white matter hyperintensities in MR images of the elderly publication-title: NeuroImage – volume: 113 start-page: 87 issue: 2 year: 2006 end-page: 91 article-title: White matter hyperintensities and cortical acetylcholinesterase activity in parkinsonian dementia publication-title: Acta Neurol. Scand. – volume: 27 start-page: 1023 issue: 8 year: 2009 end-page: 1034 article-title: Geometrical pdes based on second‐order derivatives of gauge coordinates in image processing publication-title: Image Vis. Comput. – start-page: 1 year: 1988 – volume: 17 start-page: 1 issue: 1 year: 2013 end-page: 18 article-title: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging publication-title: Med. Image Anal. – volume: 59 start-page: 3774 issue: 4 year: 2012 end-page: 3783 article-title: An automated tool for detection of flair‐hyperintense white‐matter lesions in multiple sclerosis publication-title: NeuroImage – volume: 5 start-page: 143 issue: 2 year: 2001 end-page: 156 article-title: A global optimisation method for robust affine registration of brain images publication-title: Med. Image Anal. – volume: 34 start-page: 1227 issue: 6 year: 2015 end-page: 1241 article-title: Temporal hierarchical adaptive texture crf for automatic detection of gadolinium‐enhancing multiple sclerosis lesions in brain MRI publication-title: IEEE Trans. Med. Imaging – volume: 20 start-page: 677 issue: 8 year: 2001 end-page: 688 article-title: Automated segmentation of multiple sclerosis lesions by model outlier detection publication-title: IEEE Trans. Med. Imaging – volume: 348 start-page: 1215 issue: 13 year: 2003 end-page: 1222 article-title: Silent brain infarcts and the risk of dementia and cognitive decline publication-title: N. Engl. J. Med. – volume: 24 start-page: 51 issue: 1‐2 year: 2004 end-page: 62 article-title: Impact of age‐related cerebral white matter changes on the transition to disability—The LADIS study: Rationale, design and methodology publication-title: Neuroepidemiology – volume: 31 start-page: 1181 issue: 6 year: 2012 end-page: 1194 article-title: Automatic detection of gadolinium‐enhancing multiple sclerosis lesions in brain MRI using conditional random fields publication-title: IEEE Trans. Med. Imaging – volume: 47 start-page: 145 issue: 2 year: 2000 end-page: 151 article-title: Cerebral white matter lesions and cognitive function: The rotterdam scan study publication-title: Ann. Neurol. – volume: 12 start-page: 822 issue: 8 year: 2013 end-page: 838 article-title: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration publication-title: Lancet Neurol. – volume: 55 start-page: 119 issue: 1 year: 1997 end-page: 139 article-title: A decision‐theoretic generalization of on‐line learning and an application to boosting publication-title: J. Comput. Syst. Sci. – volume: 35 start-page: 2348 issue: 5 year: 2014 end-page: 2358 article-title: Sex‐specific extent and severity of white matter damage in multiple sclerosis: Implications for cognitive decline publication-title: Hum. Brain Mapp. – volume: 3 start-page: 462 year: 2013 end-page: 469 article-title: Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN‐TTPs) publication-title: NeuroImage: Clin. – volume: 11 start-page: 29 issue: 1 year: 2011 end-page: 39 article-title: Causes and consequences of cerebral small vessel disease. The run DMC study: A prospective cohort study. Study rationale and protocol publication-title: BMC Neurol. – start-page: 1 year: 1991 end-page: 10 article-title: Note on generalization, regularization and architecture selection in nonlinear learning systems – volume: 35 start-page: 4219 issue: 8 year: 2014 end-page: 4235 article-title: Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies publication-title: Hum. Brain Mapp. – volume: 13 start-page: 1 issue: 3 year: 2015 end-page: 16 article-title: Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: A review publication-title: Neuroinformatics – volume: 44 start-page: 2787 issue: 10 year: 2013 end-page: 2794 article-title: Brain imaging and cognitive predictors of stroke and Alzheimer disease in the framingham heart study publication-title: Stroke – volume: 70 start-page: 935 issue: 12 year: 2008 end-page: 942 article-title: Association of gait and balance disorders with age‐related white matter changes the ladis study publication-title: Neurology – volume: 45 start-page: 5 issue: 1 year: 2001 end-page: 32 article-title: Random forests publication-title: Mach. Learn. – volume: 8670 start-page: 867014 year: 2013 article-title: Detection of white matter lesions in cerebral small vessel disease publication-title: Proc. SPIE – volume: 57 start-page: 416 issue: 2 year: 2011 end-page: 422 article-title: A comparison of different automated methods for the detection of white matter lesions in MRI data publication-title: NeuroImage – volume: 8 start-page: 401 issue: 5 year: 2001 end-page: 430 article-title: A four‐dimensional probabilistic atlas of the human brain publication-title: J. Am. Med. Inf. Associat. – volume: 31 start-page: 2182 issue: 9 year: 2000 end-page: 2188 article-title: Impact of white matter changes on clinical manifestation of Alzheimer's disease a quantitative study publication-title: Stroke – start-page: 111 year: 2010 end-page: 118 article-title: Spatial decision forests for MS lesion segmentation in multi‐channel MR images – volume: 63 start-page: 139 issue: 1 year: 2004 end-page: 144 article-title: White matter lesion progression a surrogate endpoint for trials in cerebral small‐vessel disease publication-title: Neurology – volume: 9 start-page: e104011 issue: 8 year: 2014 article-title: Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction publication-title: PloS One – volume: 9414 start-page: 941411 year: 2015 article-title: Small white matter lesion detection in cerebral small vessel disease publication-title: Proc. SPIE – volume: 0127 start-page: 124 year: 1977 end-page: 135 article-title: A free response approach to the measurement and characterization of radiographic observer performance publication-title: Proc. SPIE – year: 2016 – volume: 70 start-page: 9 issue: 1 year: 2001 end-page: 14 article-title: Prevalence of cerebral white matter lesions in elderly people: A population based magnetic resonance imaging study. The rotterdam scan study publication-title: J. Neurol., Neurosurg. Psychiatry – volume: 13 start-page: 89 issue: 1 year: 1998 end-page: 95 article-title: Gait disorder and parkinsonian signs in patients with stroke related to small deep infarcts and white matter lesions publication-title: Mov. Disord. – volume: 213 start-page: 138 issue: 1 year: 2013 end-page: 146 article-title: Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction publication-title: J. Neurosci. Methods – volume: 64 start-page: 273 issue: 4 year: 2008 end-page: 280 article-title: Classification of white matter lesions on magnetic resonance imaging in elderly persons publication-title: Biol. Psychiatry – volume: 20 start-page: 45 issue: 1 year: 2001 end-page: 57 article-title: Segmentation of brain MR images through a hidden markov random field model and the expectation–maximization algorithm publication-title: IEEE Trans. Med. Imaging – volume: 45 start-page: 1151 issue: 4 year: 2009 end-page: 1161 article-title: White matter lesion extension to automatic brain tissue segmentation on MRI publication-title: NeuroImage – volume: 54 start-page: 838 issue: 4 year: 2000 end-page: 842 article-title: White matter volumes and periventricular white matter hyperintensities in aging and dementia publication-title: Neurology – volume: 38 start-page: 379 issue: 3 year: 2008 end-page: 390 article-title: Fully automatic segmentation of multiple sclerosis lesions in brain MR flair images using adaptive mixtures method and markov random field model publication-title: Comput. Biol. Med. – volume: 59 start-page: 860 issue: 3 year: 2012 end-page: 871 article-title: Robust white matter lesion segmentation in flair MRI publication-title: IEEE Trans. Biomed. Eng. – volume: 21 start-page: 1037 issue: 3 year: 2004 end-page: 1044 article-title: Probabilistic segmentation of white matter lesions in MR imaging publication-title: NeuroImage – volume: 49 start-page: 1524 issue: 2 year: 2010 end-page: 1535 article-title: A topology‐preserving approach to the segmentation of brain images with multiple sclerosis lesions publication-title: NeuroImage – volume: 23 start-page: 525 issue: 5 year: 2015 end-page: 535 article-title: White matter integrity and depressive symptoms in cerebral small vessel disease: The run DMC study publication-title: Am. J. Geriatr. Psychiatry – volume: 28 start-page: 337 issue: 2 year: 2000 end-page: 407 article-title: Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors) publication-title: Ann. Stat. – volume: 22 start-page: 401 issue: 5‐6 year: 1993 end-page: 465 article-title: Brain segmentation and white matter lesion detection in MR images publication-title: Crit. Rev. Biomed. Eng. – volume: 79 start-page: 619 issue: 6 year: 2008 end-page: 624 article-title: White matter hyperintensities in late life depression: A systematic review publication-title: J. Neurol., Neurosurg. Psychiatry – ident: e_1_2_8_22_1 doi: 10.1016/j.neuroimage.2009.09.005 – ident: e_1_2_8_56_1 doi: 10.1038/s41598-017-05300-5 – ident: e_1_2_8_30_1 doi: 10.1117/12.2007940 – ident: e_1_2_8_53_1 doi: 10.1006/jcss.1997.1504 – ident: e_1_2_8_2_1 doi: 10.1136/jnnp.70.1.9 – ident: e_1_2_8_50_1 doi: 10.1109/EMBC.2012.6347208 – ident: e_1_2_8_40_1 doi: 10.1097/00004728‐197906000‐00001 – ident: e_1_2_8_39_1 doi: 10.1212/01.WNL.0000132635.75819.E5 – ident: e_1_2_8_43_1 doi: 10.1016/j.biopsych.2008.03.024 – ident: e_1_2_8_16_1 doi: 10.1212/WNL.57.6.990 – ident: e_1_2_8_23_1 doi: 10.1109/42.938237 – ident: e_1_2_8_18_1 doi: 10.1016/j.neuroimage.2005.06.061 – ident: e_1_2_8_19_1 doi: 10.1016/j.nicl.2015.05.003 – ident: e_1_2_8_28_1 doi: 10.1016/j.neuroimage.2011.04.053 – ident: e_1_2_8_24_1 doi: 10.1016/j.compbiomed.2007.12.005 – ident: e_1_2_8_7_1 doi: 10.1002/mds.870130119 – ident: e_1_2_8_8_1 doi: 10.1056/NEJMoa022066 – ident: e_1_2_8_37_1 doi: 10.1007/s12021‐015‐9260‐y – ident: e_1_2_8_34_1 doi: 10.1016/j.neuroimage.2003.10.012 – ident: e_1_2_8_6_1 doi: 10.1016/j.jagp.2014.07.002 – ident: e_1_2_8_20_1 doi: 10.1109/TBME.2011.2181167 – ident: e_1_2_8_27_1 doi: 10.1371/journal.pone.0104011 – ident: e_1_2_8_14_1 doi: 10.1161/STROKEAHA.113.000947 – ident: e_1_2_8_45_1 doi: 10.1136/jamia.2001.0080401 – ident: e_1_2_8_36_1 doi: 10.1109/TMI.2014.2382561 – ident: e_1_2_8_3_1 doi: 10.1016/S1474‐4422(13)70124‐8 – ident: e_1_2_8_51_1 doi: 10.1016/j.imavis.2008.09.003 – ident: e_1_2_8_52_1 doi: 10.1023/A:1010933404324 – ident: e_1_2_8_33_1 doi: 10.1007/978-3-642-15705-9_14 – ident: e_1_2_8_29_1 doi: 10.1002/hbm.22472 – ident: e_1_2_8_9_1 doi: 10.1159/000081050 – ident: e_1_2_8_13_1 doi: 10.1212/WNL.54.4.838 – ident: e_1_2_8_4_1 doi: 10.1212/01.wnl.0000305959.46197.e6 – ident: e_1_2_8_21_1 doi: 10.1016/j.jneumeth.2012.12.014 – ident: e_1_2_8_38_1 doi: 10.1016/j.media.2012.09.004 – ident: e_1_2_8_46_1 doi: 10.1002/hbm.10062 – ident: e_1_2_8_15_1 doi: 10.1111/j.1600‐0404.2005.00553.x – ident: e_1_2_8_10_1 doi: 10.1186/1471‐2377‐11‐29 – ident: e_1_2_8_25_1 doi: 10.1016/j.neuroimage.2009.01.011 – ident: e_1_2_8_32_1 doi: 10.1109/TMI.2012.2186639 – ident: e_1_2_8_49_1 doi: 10.1117/12.2081597 – ident: e_1_2_8_5_1 doi: 10.1002/1531‐8249(200002)47:2<145::AID‐ANA3>3.0.CO;2‐P – ident: e_1_2_8_26_1 doi: 10.1016/j.neuroimage.2011.11.032 – ident: e_1_2_8_12_1 doi: 10.1161/01.STR.31.9.2182 – ident: e_1_2_8_42_1 doi: 10.1117/12.955926 – ident: e_1_2_8_11_1 doi: 10.1002/hbm.22332 – ident: e_1_2_8_44_1 doi: 10.1016/S1361‐8415(01)00036‐6 – ident: e_1_2_8_17_1 doi: 10.1136/jnnp.2007.124651 – volume: 22 start-page: 401 issue: 5 year: 1993 ident: e_1_2_8_31_1 article-title: Brain segmentation and white matter lesion detection in MR images publication-title: Crit. Rev. Biomed. Eng. – start-page: 1 volume-title: Mixture Models. Inference and Applications to Clustering, Statistics: Textbooks and Monographs year: 1988 ident: e_1_2_8_48_1 – ident: e_1_2_8_35_1 doi: 10.1016/j.nicl.2013.10.003 – ident: e_1_2_8_47_1 doi: 10.1109/42.906424 – ident: e_1_2_8_41_1 doi: 10.1109/NNSP.1991.239541 – ident: e_1_2_8_55_1 doi: 10.1109/ISBI.2016.7493532 – ident: e_1_2_8_54_1 doi: 10.1214/aos/1016218223 |
| SSID | ssj0006350 |
| Score | 2.4620287 |
| Snippet | Purpose:
White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism,... Purpose: White matter hyperintensities (WMH) are seen on FLAIR‐MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism,... White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and... |
| SourceID | proquest pubmed crossref wiley scitation |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 6246 |
| SubjectTerms | adaboost automated detection Automation Biological material, e.g. blood, urine; Haemocytometers biological tissues biomedical MRI brain Cerebral Small Vessel Diseases - diagnostic imaging computer aided detection Dementia Digital computing or data processing equipment or methods, specially adapted for specific applications diseases Humans Image data processing or generation, in general Image Processing, Computer-Assisted - methods image segmentation In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines Inference methods or devices Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging Learning learning (artificial intelligence) Machine learning Magnetic Resonance Imaging medical disorders medical image processing Medical magnetic resonance imaging neurophysiology Segmentation Small vessel disease Systems analysis Testing procedures Tissues White Matter - diagnostic imaging white matter hyperintensity |
| Title | Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease |
| URI | http://dx.doi.org/10.1118/1.4966029 https://onlinelibrary.wiley.com/doi/abs/10.1118%2F1.4966029 https://www.ncbi.nlm.nih.gov/pubmed/27908171 https://www.proquest.com/docview/1845827208 |
| Volume | 43 |
| WOSCitedRecordID | wos000390237200005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 2473-4209 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006350 issn: 0094-2405 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB61W47ywFGOhqMyh1BfArGTTWzxVAErHtqqQhTtWxRf6krbpNoLwa9nJs4GVRSExFOU6IsTZTz2TL7xZ4BX1trMpORIIiFRbZHGWjkfJyaVzheZ1-067q-HxfGxHI_VyQa8W6-FCfoQ_Q838ox2vCYHr3S3CwmnwnX-JiNlSaE2YUtgv80GsPXh8-j0sB-IcS4NK1BURiTCsBMWwtvf9jdfno5-izFvwU2ciQIpfjl8beef0Z3_evO7cLsLO9lB6Cf3YMPVO3DjqCPWd-B6Wwlq5vdBHywXDYaxzjLrFm2hVs0az74R38DOWzlOdvadBJJD9TspshKgmk7ZfPIDTyY1M25GjDReOafrK1Ion7KODXoAp6OPX95_iruNGGKD2YmK5TAv0tSa1HJfJF4Zm_OqQCtTMpioiphkrnlhvBcIUFluEsd5jlCdCMPThzCom9rtAhMSAY5Lk2LcKYXXTmdKp5hFYuhhhYlgf22Pcv3habOMaRmyFVnysvt6EbzooRdBmuMq0PO1UUt0HGJDqto1y3mJqe1QEgstI3gUrN03IwqFoVLBI3jZm_9vz7gCtWpmvxDlhfURvG47xZ_bKY9O6PD4X4FPYBvDtzwU1zyFwWK2dM_gmlktJvPZHmwWY7nX-cRPHs4Itw |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB6VLVB44ChXOM0hxEsgtrNJLPFSAasidlcValHfrI0PsdI2qfZC8OuZibNBFQUh8RQl-pxEGY8942_8BeCFtTY1khxJJCSqLWRcKufjxMjC-Tz1ZbOP-8swH4-L42N1sAVvN3thgj5Et-BGntGM1-TgtCDdejlVrvPXKUlLCnUBtlPsRv0ebL__PDgadiMxTqZhC4pKiUXot8pC2PxN1_jsfPRbkHkVdnAqCqz42fi1mYAG1__v1W_AtTbwZHuhp9yELVftwuVRS63vwqWmFtQsbkG5t1rWGMg6y6xbNqVaFas9-0aMAztpBDnZ1-8kkRzq30mTlQCT2Ywtpj_wZFox4-bESeOVE7q-Jo3yGWv5oNtwNPhw-G4_bn_FEBvMT1Rc9LNcSmuk5T5PvDI245Mc7UzpYKImxCXzkufGe4EAlWYmcZxnCC0TYbi8A72qrtw9YKJAgOOFkRh5FsKXrkxVKTGPxODDChPBq41B9ObL0-8yZjrkK4Xmuv16ETzroKdBnOM80NONVTW6DvEhk8rVq4XG5LZfEA9dRHA3mLu7jcgVBks5j-B5Z_-_PeMc1Lqe_0LoU-sjeNn0ij_fR48O6HD_X4FPYGf_cDTUw4_jTw_gCgZzWSi1eQi95XzlHsFFs15OF_PHrWv8BMLBC78 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwEB6VFgo8cLQc4TSHEC-B2M4mtsRLRVmB2K5WiFZ9szY-1JW2SbUXgl_PTJINqigIiaco0eck8njimXzjzwAvnXOpleRIIiFRbSHjQvsQJ1YqH_I0FPU67qNBPhyq42M92oB367UwjT5E98ONPKP-XpOD-zMXWi-nynX-JiVpSaEvwVba0xm65db-l_7hoPsS42TaLEHRKbEIvVZZCJu_7Rqfn49-CzKvw1WcihpW_Hz8Wk9A_Zv_9-q34EYbeLK9ZqTchg1f7sD2QUut78CVuhbUzneh2FsuKgxkvWPOL-pSrZJVgX0jxoGd1oKc7OQ7SSQ39e-kyUqA8XTK5pMfeDIpmfUz4qTxyildX5FG-ZS1fNAdOOx_-Pr-Y9xuxRBbzE90rHpZLqWz0vGQJ0Fbl_FxjnamdDDRY-KSecFzG4JAgE4zm3jOM4QWibBc3oXNsir9fWBCIcBzZSVGnkqEwhepLiTmkRh8OGEjeL02iFn3PG2XMTVNvqIMN23vRfC8g5414hwXgZ6trWrQdYgPGZe-Ws4NJrc9RTy0iuBeY-7uNiLXGCzlPIIXnf3_9owLUKtq9gthcABE8KoeFX--jzkY0eHBvwKfwvZov28Gn4afH8I1jOWyptLmEWwuZkv_GC7b1WIynz1pPeMnOywLOg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Automated+detection+of+white+matter+hyperintensities+of+all+sizes+in+cerebral+small+vessel+disease&rft.jtitle=Medical+physics+%28Lancaster%29&rft.au=Ghafoorian%2C+Mohsen&rft.au=Karssemeijer%2C+Nico&rft.au=van+Uden%2C+Inge+W.+M.&rft.au=de+Leeuw%2C+Frank%E2%80%90Erik&rft.date=2016-12-01&rft.issn=0094-2405&rft.eissn=2473-4209&rft.volume=43&rft.issue=12&rft.spage=6246&rft.epage=6258&rft_id=info:doi/10.1118%2F1.4966029&rft.externalDBID=n%2Fa&rft.externalDocID=10_1118_1_4966029 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0094-2405&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0094-2405&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0094-2405&client=summon |