Feasibility of automated pancreas segmentation based on dynamic MRI
MRI-guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment two-dimensional (2D) upper abd...
Uložené v:
| Vydané v: | British journal of radiology Ročník 87; číslo 1044; s. 20140248 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
The British Institute of Radiology
01.12.2014
|
| Predmet: | |
| ISSN: | 0007-1285, 1748-880X, 1748-880X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | MRI-guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment two-dimensional (2D) upper abdominal dynamic MRI (dMRI) is developed for this purpose.
2D coronal dynamic MR images of two healthy volunteers were acquired with a frame rate of 5 frames per second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centres of the ROIs were manually annotated. These centre locations were propagated to the next dMRI frame. Four-neighborhood region transfer growth was performed from these initial seeds before refinement using shape constraints. RESULTS from hGReS and two other automated segmentation methods using integrated edge detection and region growth (IER) and level set, respectively, were compared with manual contours using Dice's index (DI).
For the first patient, the hGReS resulted in the organ segmentation accuracy as a measure by the DI (0.77) for the pancreas, superior to the level set method (0.72) and IER (0.71). The hGReS was shown to be reproducible on the second subject, achieving a DI of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively. Motion trajectories derived from the hGReS were highly correlated to respiratory motion.
We have shown the feasibility of automated segmentation of the pancreas anatomy on dMRI.
Using the hybrid method improves segmentation robustness of low-contrast images. |
|---|---|
| AbstractList | MRI-guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment two-dimensional (2D) upper abdominal dynamic MRI (dMRI) is developed for this purpose.OBJECTIVEMRI-guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment two-dimensional (2D) upper abdominal dynamic MRI (dMRI) is developed for this purpose.2D coronal dynamic MR images of two healthy volunteers were acquired with a frame rate of 5 frames per second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centres of the ROIs were manually annotated. These centre locations were propagated to the next dMRI frame. Four-neighborhood region transfer growth was performed from these initial seeds before refinement using shape constraints. RESULTS from hGReS and two other automated segmentation methods using integrated edge detection and region growth (IER) and level set, respectively, were compared with manual contours using Dice's index (DI).METHODS2D coronal dynamic MR images of two healthy volunteers were acquired with a frame rate of 5 frames per second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centres of the ROIs were manually annotated. These centre locations were propagated to the next dMRI frame. Four-neighborhood region transfer growth was performed from these initial seeds before refinement using shape constraints. RESULTS from hGReS and two other automated segmentation methods using integrated edge detection and region growth (IER) and level set, respectively, were compared with manual contours using Dice's index (DI).For the first patient, the hGReS resulted in the organ segmentation accuracy as a measure by the DI (0.77) for the pancreas, superior to the level set method (0.72) and IER (0.71). The hGReS was shown to be reproducible on the second subject, achieving a DI of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively. Motion trajectories derived from the hGReS were highly correlated to respiratory motion.RESULTSFor the first patient, the hGReS resulted in the organ segmentation accuracy as a measure by the DI (0.77) for the pancreas, superior to the level set method (0.72) and IER (0.71). The hGReS was shown to be reproducible on the second subject, achieving a DI of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively. Motion trajectories derived from the hGReS were highly correlated to respiratory motion.We have shown the feasibility of automated segmentation of the pancreas anatomy on dMRI.CONCLUSIONWe have shown the feasibility of automated segmentation of the pancreas anatomy on dMRI.Using the hybrid method improves segmentation robustness of low-contrast images.ADVANCES IN KNOWLEDGEUsing the hybrid method improves segmentation robustness of low-contrast images. MRI-guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment two-dimensional (2D) upper abdominal dynamic MRI (dMRI) is developed for this purpose. 2D coronal dynamic MR images of two healthy volunteers were acquired with a frame rate of 5 frames per second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centres of the ROIs were manually annotated. These centre locations were propagated to the next dMRI frame. Four-neighborhood region transfer growth was performed from these initial seeds before refinement using shape constraints. RESULTS from hGReS and two other automated segmentation methods using integrated edge detection and region growth (IER) and level set, respectively, were compared with manual contours using Dice's index (DI). For the first patient, the hGReS resulted in the organ segmentation accuracy as a measure by the DI (0.77) for the pancreas, superior to the level set method (0.72) and IER (0.71). The hGReS was shown to be reproducible on the second subject, achieving a DI of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively. Motion trajectories derived from the hGReS were highly correlated to respiratory motion. We have shown the feasibility of automated segmentation of the pancreas anatomy on dMRI. Using the hybrid method improves segmentation robustness of low-contrast images. |
| Author | Hu, P Rapacchi, S Wu, J Liu, F Gou, S Lee, P Sheng, K |
| Author_xml | – sequence: 1 givenname: S surname: Gou fullname: Gou, S – sequence: 2 givenname: J surname: Wu fullname: Wu, J – sequence: 3 givenname: F surname: Liu fullname: Liu, F – sequence: 4 givenname: P surname: Lee fullname: Lee, P – sequence: 5 givenname: S surname: Rapacchi fullname: Rapacchi, S – sequence: 6 givenname: P surname: Hu fullname: Hu, P – sequence: 7 givenname: K surname: Sheng fullname: Sheng, K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25270713$$D View this record in MEDLINE/PubMed |
| BookMark | eNptUU1LAzEUDFKxH3rzLHv04NZ8bTd7EaRYLVQEUfAW3ibZmrK7qZtdof_elLai4unlMfNmwswQ9WpXG4TOCR4TmmTX-aoZU0w4plwcoQFJuYiFwG89NMAYpzGhIumjofer7Zpk-AT1aUJTnBI2QNOZAW9zW9p2E7kigq51FbRGR2uoVRPAyJtlZeoWWuvqKAcfsPDQmxoqq6LH5_kpOi6g9OZsP0fodXb3Mn2IF0_38-ntIlZM8DbO1cQIKLTAhFKhDUtIVuSTVHCd44SxggJkWgvDeaY05HiSZJQranABGRjMRuhmp7vu8spoFT7VQCnXja2g2UgHVv5Gavsul-5TcsoZJSwIXO4FGvfRGd_KynplyhJq4zovyWQbC2dJFqgXP72-TQ7JBQLdEVTjvG9MIZXdZRSsbSkJltt6ZKhHHuoJR1d_jg66_9K_AEfpklc |
| CitedBy_id | crossref_primary_10_1038_s41598_020_61759_9 crossref_primary_10_1016_j_ijrobp_2017_04_023 crossref_primary_10_1016_j_compmedimag_2019_04_004 crossref_primary_10_1177_2058460119834690 crossref_primary_10_1002_mp_15534 crossref_primary_10_1259_bjr_20180267 crossref_primary_10_1016_j_phro_2018_12_003 crossref_primary_10_1088_1361_6560_aaebcf |
| Cites_doi | 10.1016/j.ijrobp.2005.07.002 10.1088/0031-9155/57/5/1349 10.1118/1.3457329 10.1097/COC.0b013e31821f876a 10.1088/0031-9155/53/4/006 10.1016/j.radonc.2005.01.008 10.1053/j.semradonc.2003.10.006 10.1002/jmri.23813 10.1016/j.ijrobp.2011.12.073 10.1118/1.3121425 10.1016/j.ijrobp.2007.06.062 10.1016/j.clon.2009.07.015 10.1016/j.ijrobp.2003.11.004 10.1109/TSMC.1979.4310076 10.1016/j.neuroimage.2012.01.076 10.1109/34.368173 10.1016/j.ijrobp.2012.09.014 10.1088/0031-9155/58/7/2235 10.1118/1.2900108 10.1016/j.ijrobp.2010.05.006 10.1038/sj.bjc.6605420 10.1006/jcph.2000.6636 10.1109/34.49050 10.1155/2012/713073 10.1016/S0165-1684(97)00061-3 10.1016/j.ijrobp.2011.08.026 10.1016/j.radonc.2007.06.008 10.1109/TMI.2013.2265805 10.1088/0031-9155/51/3/010 10.4240/wjgs.v4.i5.104 10.1073/pnas.93.4.1591 10.1016/j.ijrobp.2007.11.042 10.1118/1.2982245 10.1016/j.ijrobp.2011.09.008 10.1109/TIP.2010.2096950 10.1016/j.ijrobp.2007.07.2322 10.1088/0031-9155/55/18/009 10.1109/TIP.2010.2041414 10.1002/cncr.21747 |
| ContentType | Journal Article |
| Copyright | 2014 The Authors. Published by the British Institute of Radiology 2014 The British Institute of Radiology |
| Copyright_xml | – notice: 2014 The Authors. Published by the British Institute of Radiology 2014 The British Institute of Radiology |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM |
| DOI | 10.1259/bjr.20140248 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| 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 |
| DocumentTitleAlternate | S Gou et al |
| EISSN | 1748-880X |
| ExternalDocumentID | PMC4243213 25270713 10_1259_bjr_20140248 |
| Genre | Journal Article Comparative Study Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NCI NIH HHS grantid: R21CA161670 |
| GroupedDBID | --- .55 .GJ 0R~ 169 18M 1CY 1KJ 1OC 23N 2WC 33P 34G 36B 39C 4.4 53G 5GY 5RE 5WD 6J9 AANLZ AASGY AAUAY AAWTL AAXRX AAYXX ABCUV ABDFA ABEFU ABEJV ABGNP ABJNI ABNHQ ABQNK ABSZQ ABXGK ABXVV ACAHQ ACCZN ACGFO ACGOF ACVCV ACXBN ACZBC ADBBV ADBTR ADIPN ADMGS ADNBA ADOZA ADVOB ADXAS AEGXH AENEX AEUYR AFFNX AFFQV AGMDO AHGBF AHMMS AI. AIACR AIAGR AIURR AJAOE AJBYB AJDVS AJNCP ALMA_UNASSIGNED_HOLDINGS AMYDB AOIJS AVNTJ BAWUL BCRHZ BFHJK C1A C45 CITATION CS3 DCZOG DIK DRFUL DRMAN DRSTM DU5 E3Z EBD EBS EJD EMB EMOBN F5P GX1 H13 HYE IH2 J5H K-O KBYEO KOP L7B LATKE LEEKS LYRES MXFUL MXMAN MXSTM N4W NU- OCZFY OJZSN OK1 OVD OWPYF P0W P2P RJQFR ROX SJN SUPJJ SV3 TEORI TR2 TUS TWZ TXR VH1 W8F WIN WOQ X7M ZGI ZXP ZZTAW AGORE ALUQN CGR CUY CVF ECM EIF NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c384t-bc6e8afd801228de3519fb6784db0533f2aa9dd8e449cdab065924c2e0fa9ae03 |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000345423600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0007-1285 1748-880X |
| IngestDate | Tue Nov 04 01:56:31 EST 2025 Sun Nov 09 09:40:14 EST 2025 Mon Jul 21 06:01:12 EDT 2025 Sat Nov 29 06:21:40 EST 2025 Tue Nov 18 20:49:40 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1044 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c384t-bc6e8afd801228de3519fb6784db0533f2aa9dd8e449cdab065924c2e0fa9ae03 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/4243213 |
| PMID | 25270713 |
| PQID | 1627074359 |
| PQPubID | 23479 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_4243213 proquest_miscellaneous_1627074359 pubmed_primary_25270713 crossref_citationtrail_10_1259_bjr_20140248 crossref_primary_10_1259_bjr_20140248 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-12-01 |
| PublicationDateYYYYMMDD | 2014-12-01 |
| PublicationDate_xml | – month: 12 year: 2014 text: 2014-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | British journal of radiology |
| PublicationTitleAlternate | Br J Radiol |
| PublicationYear | 2014 |
| Publisher | The British Institute of Radiology |
| Publisher_xml | – name: The British Institute of Radiology |
| References | b10 b32 b31 b12 b34 b11 b33 b14 b36 b13 b35 b16 b38 b15 b37 b18 b17 b39 b19 b1 b3 b4 b5 Krechler T (b2) 2011; 150 b6 b7 b8 b9 b41 b40 b21 b20 b22 b25 b24 b27 b26 b29 b28 b30 14752736 - Semin Radiat Oncol. 2004 Jan;14(1):81-90 23744670 - IEEE Trans Med Imaging. 2013 Sep;32(9):1723-30 20879555 - Med Phys. 2010 Aug;37(8):3927-34 17692979 - Radiother Oncol. 2007 Sep;84(3):279-82 18263948 - Phys Med Biol. 2008 Feb 21;53(4):909-23 23475278 - Phys Med Biol. 2013 Apr 7;58(7):2235-45 18313530 - Int J Radiat Oncol Biol Phys. 2008 Mar 15;70(4):1229-38 11607632 - Proc Natl Acad Sci U S A. 1996 Feb 20;93(4):1591-5 15001240 - Int J Radiat Oncol Biol Phys. 2004 Mar 15;58(4):1017-21 22292339 - Cas Lek Cesk. 2011;150(11):587-93 17889270 - Int J Radiat Oncol Biol Phys. 2007 Nov 1;69(3):895-902 17889271 - Int J Radiat Oncol Biol Phys. 2007 Nov 1;69(3):903-9 20801742 - IEEE Trans Image Process. 2010 Dec;19(12):3243-54 23150822 - Pulm Med. 2012;2012:713073 22655124 - World J Gastrointest Surg. 2012 May 27;4(5):104-13 19610310 - Med Phys. 2009 Jun;36(6):2215-21 22349351 - Phys Med Biol. 2012 Mar 7;57(5):1349-58 16424585 - Phys Med Biol. 2006 Feb 7;51(3):617-36 21659830 - Am J Clin Oncol. 2012 Dec;35(6):537-42 16086906 - Radiother Oncol. 2005 May;75(2):149-56 18561647 - Med Phys. 2008 May;35(5):1718-33 22436785 - Int J Radiat Oncol Biol Phys. 2012 Jul 1;83(3):e423-9 16168826 - Int J Radiat Oncol Biol Phys. 2005 Oct 1;63(2):320-3 19733469 - Clin Oncol (R Coll Radiol). 2009 Nov;21(9):713-9 19904268 - Br J Cancer. 2009 Dec 1;101(11):1853-9 23102840 - Int J Radiat Oncol Biol Phys. 2013 Mar 15;85(4):999-1005 21549517 - Int J Radiat Oncol Biol Phys. 2011 Sep 1;81(1):181-8 22172900 - Int J Radiat Oncol Biol Phys. 2012 Jul 1;83(3):909-15 19070231 - Med Phys. 2008 Nov;35(11):4974-81 23011805 - J Magn Reson Imaging. 2013 Feb;37(2):423-30 16475150 - Cancer. 2006 Mar 15;106(6):1347-52 22284684 - Int J Radiat Oncol Biol Phys. 2012 Jul 1;83(3):916-20 20736500 - Phys Med Biol. 2010 Sep 21;55(18):5401-15 22293133 - Neuroimage. 2012 Apr 2;60(2):1025-35 |
| References_xml | – ident: b3 doi: 10.1016/j.ijrobp.2005.07.002 – ident: b20 doi: 10.1088/0031-9155/57/5/1349 – ident: b15 doi: 10.1118/1.3457329 – ident: b7 doi: 10.1097/COC.0b013e31821f876a – ident: b21 doi: 10.1088/0031-9155/53/4/006 – ident: b33 doi: 10.1016/j.radonc.2005.01.008 – ident: b35 doi: 10.1053/j.semradonc.2003.10.006 – ident: b40 doi: 10.1002/jmri.23813 – ident: b10 doi: 10.1016/j.ijrobp.2011.12.073 – ident: b34 doi: 10.1118/1.3121425 – ident: b12 doi: 10.1016/j.ijrobp.2007.06.062 – ident: b11 doi: 10.1016/j.clon.2009.07.015 – ident: b4 doi: 10.1016/j.ijrobp.2003.11.004 – ident: b26 doi: 10.1109/TSMC.1979.4310076 – ident: b41 doi: 10.1016/j.neuroimage.2012.01.076 – volume: 150 start-page: 587 year: 2011 ident: b2 publication-title: Cas Lek Cesk – ident: b29 doi: 10.1109/34.368173 – ident: b14 doi: 10.1016/j.ijrobp.2012.09.014 – ident: b19 doi: 10.1088/0031-9155/58/7/2235 – ident: b37 doi: 10.1118/1.2900108 – ident: b5 doi: 10.1016/j.ijrobp.2010.05.006 – ident: b13 doi: 10.1038/sj.bjc.6605420 – ident: b27 doi: 10.1006/jcph.2000.6636 – ident: b24 doi: 10.1109/34.49050 – ident: b8 doi: 10.1155/2012/713073 – ident: b22 doi: 10.1016/S0165-1684(97)00061-3 – ident: b6 doi: 10.1016/j.ijrobp.2011.08.026 – ident: b18 doi: 10.1016/j.radonc.2007.06.008 – ident: b39 doi: 10.1109/TMI.2013.2265805 – ident: b36 doi: 10.1088/0031-9155/51/3/010 – ident: b9 doi: 10.4240/wjgs.v4.i5.104 – ident: b28 doi: 10.1073/pnas.93.4.1591 – ident: b32 doi: 10.1016/j.ijrobp.2007.11.042 – ident: b16 doi: 10.1118/1.2982245 – ident: b1 doi: 10.1016/j.ijrobp.2011.09.008 – ident: b25 doi: 10.1109/TIP.2010.2096950 – ident: b17 doi: 10.1016/j.ijrobp.2007.07.2322 – ident: b31 doi: 10.1088/0031-9155/55/18/009 – ident: b30 doi: 10.1109/TIP.2010.2041414 – ident: b38 doi: 10.1002/cncr.21747 – reference: 17889270 - Int J Radiat Oncol Biol Phys. 2007 Nov 1;69(3):895-902 – reference: 18313530 - Int J Radiat Oncol Biol Phys. 2008 Mar 15;70(4):1229-38 – reference: 20879555 - Med Phys. 2010 Aug;37(8):3927-34 – reference: 17692979 - Radiother Oncol. 2007 Sep;84(3):279-82 – reference: 22655124 - World J Gastrointest Surg. 2012 May 27;4(5):104-13 – reference: 23011805 - J Magn Reson Imaging. 2013 Feb;37(2):423-30 – reference: 22292339 - Cas Lek Cesk. 2011;150(11):587-93 – reference: 19610310 - Med Phys. 2009 Jun;36(6):2215-21 – reference: 17889271 - Int J Radiat Oncol Biol Phys. 2007 Nov 1;69(3):903-9 – reference: 15001240 - Int J Radiat Oncol Biol Phys. 2004 Mar 15;58(4):1017-21 – reference: 21549517 - Int J Radiat Oncol Biol Phys. 2011 Sep 1;81(1):181-8 – reference: 21659830 - Am J Clin Oncol. 2012 Dec;35(6):537-42 – reference: 23150822 - Pulm Med. 2012;2012:713073 – reference: 23102840 - Int J Radiat Oncol Biol Phys. 2013 Mar 15;85(4):999-1005 – reference: 18561647 - Med Phys. 2008 May;35(5):1718-33 – reference: 19733469 - Clin Oncol (R Coll Radiol). 2009 Nov;21(9):713-9 – reference: 22172900 - Int J Radiat Oncol Biol Phys. 2012 Jul 1;83(3):909-15 – reference: 14752736 - Semin Radiat Oncol. 2004 Jan;14(1):81-90 – reference: 22349351 - Phys Med Biol. 2012 Mar 7;57(5):1349-58 – reference: 22436785 - Int J Radiat Oncol Biol Phys. 2012 Jul 1;83(3):e423-9 – reference: 22293133 - Neuroimage. 2012 Apr 2;60(2):1025-35 – reference: 20801742 - IEEE Trans Image Process. 2010 Dec;19(12):3243-54 – reference: 19070231 - Med Phys. 2008 Nov;35(11):4974-81 – reference: 16475150 - Cancer. 2006 Mar 15;106(6):1347-52 – reference: 16424585 - Phys Med Biol. 2006 Feb 7;51(3):617-36 – reference: 16086906 - Radiother Oncol. 2005 May;75(2):149-56 – reference: 19904268 - Br J Cancer. 2009 Dec 1;101(11):1853-9 – reference: 18263948 - Phys Med Biol. 2008 Feb 21;53(4):909-23 – reference: 23475278 - Phys Med Biol. 2013 Apr 7;58(7):2235-45 – reference: 20736500 - Phys Med Biol. 2010 Sep 21;55(18):5401-15 – reference: 23744670 - IEEE Trans Med Imaging. 2013 Sep;32(9):1723-30 – reference: 11607632 - Proc Natl Acad Sci U S A. 1996 Feb 20;93(4):1591-5 – reference: 16168826 - Int J Radiat Oncol Biol Phys. 2005 Oct 1;63(2):320-3 – reference: 22284684 - Int J Radiat Oncol Biol Phys. 2012 Jul 1;83(3):916-20 |
| SSID | ssj0007590 |
| Score | 2.143661 |
| Snippet | MRI-guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated... |
| SourceID | pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 20140248 |
| SubjectTerms | Automation - methods Feasibility Studies Gastrointestinal/Abdominal Healthy Volunteers Humans Magnetic Resonance Imaging - methods Motion Pancreas - anatomy & histology Physics and Technology Radiotherapy and Oncology |
| Title | Feasibility of automated pancreas segmentation based on dynamic MRI |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/25270713 https://www.proquest.com/docview/1627074359 https://pubmed.ncbi.nlm.nih.gov/PMC4243213 |
| Volume | 87 |
| WOSCitedRecordID | wos000345423600005&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 Free Content customDbUrl: eissn: 1748-880X dateEnd: 20231231 omitProxy: false ssIdentifier: ssj0007590 issn: 0007-1285 databaseCode: WIN dateStart: 20090101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1748-880X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007590 issn: 0007-1285 databaseCode: DRFUL dateStart: 20090101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwED9BB4gXBOOrfExBgqcqLHO92nlEhQFSO1VVJ_oWObYDRSyp2gbtz-f8kSzehARIvESR69iV73wfvvPvAF4rJniBpm4sRomIKSd5jH6IjFFXCNQvBlHOgrhO2OkpXy7TmT9V2tpyAqws-cVFuv6vpMY2JLa5OvsX5G4HxQZ8R6LjE8mOzz8iPBp1PuXVBs9FvavQKkW7Eve9tCnoW_313F85KgdGjSkTMlCuNv1gOv8cRHo96lEHYmIj1Co4jf9Y1cEp6pc6CDhNVnWQQ-yTf2bdE4cjeiV7Y-ETF8zcQUbDPJi9EbgMP3dVed5qJ2MZ5TGKjWVXCHut65ktcZiQ18Q7-mq4_Pl3A-SKniFxIJ0doq7PLVXJMWHG_b5Ucm3q4Ww6pgQZ0FQ63iPsOOU92Hs_PzmbtAocG93NJf_f_X0JnPywO7XBkfbzhEbNNU_lasJtx4JZ3Id73vWI3jmWeQA3dLkPd6Y-uWIfbttsYLl9COMOD0VVEbU8FDU8FHV5KLI8FOGL56EIeegRnJ18WIw_xb7aRiyHnO7iXI40F4UyJgvhSpvKjUWOtgxVubmwXRAhUqW4pjSVSuQ2IE8l0UlhEN6T4WPolVWpn0KU4CB8qFIuBVqAMkkThZKADbnmjNAR78OgWa5Meih6UxHlR2ZcUlznDNc5a9a5D2_a3msHwfKbfq-alc9QRprAlyh1VW-zo5EhEzoGaR-eOEq0IzUk7AMLaNR2MPjr4S_l6pvFYfd89Oyfv3wOdy_31wvo7Ta1fgm35E_cW5sDuMmW_MAz5y8J2Ksn |
| linkProvider | Wiley-Blackwell |
| 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=Feasibility+of+automated+pancreas+segmentation+based+on+dynamic+MRI&rft.jtitle=British+journal+of+radiology&rft.au=Gou%2C+S&rft.au=Wu%2C+J&rft.au=Liu%2C+F&rft.au=Lee%2C+P&rft.date=2014-12-01&rft.pub=The+British+Institute+of+Radiology&rft.issn=0007-1285&rft.eissn=1748-880X&rft.volume=87&rft.issue=1044&rft_id=info:doi/10.1259%2Fbjr.20140248&rft_id=info%3Apmid%2F25270713&rft.externalDocID=PMC4243213 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0007-1285&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0007-1285&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0007-1285&client=summon |