Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound
Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled...
Uloženo v:
| Vydáno v: | Physics in medicine & biology Ročník 67; číslo 7 |
|---|---|
| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
IOP Publishing
07.04.2022
|
| Témata: | |
| ISSN: | 1361-6560, 1361-6560 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance. |
|---|---|
| AbstractList | Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance.Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance. Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance. |
| Author | Fenster, Aaron D’Souza, David Gillies, Derek J Cool, Derek W Guo, Fumin Orlando, Nathan Hoover, Douglas A Romagnoli, Cesare Gyacskov, Igor |
| Author_xml | – sequence: 1 givenname: Nathan surname: Orlando fullname: Orlando, Nathan organization: Western University Robarts Research Institute, London, Ontario N6A 3K7, Canada – sequence: 2 givenname: Igor surname: Gyacskov fullname: Gyacskov, Igor organization: Western University Robarts Research Institute, London, Ontario N6A 3K7, Canada – sequence: 3 givenname: Derek J surname: Gillies fullname: Gillies, Derek J organization: London Health Sciences Centre , London, Ontario N6A 5W9, Canada – sequence: 4 givenname: Fumin surname: Guo fullname: Guo, Fumin organization: University of Toronto Department of Medical Biophysics, Toronto, Ontario M4N 3M5, Canada – sequence: 5 givenname: Cesare surname: Romagnoli fullname: Romagnoli, Cesare organization: Western University Department of Medical Imaging, London, Ontario N6A 3K7, a Canad – sequence: 6 givenname: David surname: D’Souza fullname: D’Souza, David organization: Western University Department of Oncology, London, Ontario N6A 3K7, Canada – sequence: 7 givenname: Derek W surname: Cool fullname: Cool, Derek W organization: Western University Department of Medical Imaging, London, Ontario N6A 3K7, a Canad – sequence: 8 givenname: Douglas A surname: Hoover fullname: Hoover, Douglas A organization: Western University Department of Oncology, London, Ontario N6A 3K7, Canada – sequence: 9 givenname: Aaron surname: Fenster fullname: Fenster, Aaron organization: Western University Department of Oncology, London, Ontario N6A 3K7, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35240585$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkctP3DAQxq2KqjzaO6fKNzhsih07sTkinpWQeoGzNYnHK6PEDrFz2Er93_FqF9QDp3noN6Nv5jsmByEGJOSUs1-caX3BRcurtmnZBfQNXIov5OijdfBffkiOU3phjHNdy2_kUDS1ZI1ujsi_W-ewzzQ6aiFDwkyT_4sr6kdYI31dYPB5s6IQ7L6VNxPSGKhFnOiAMAcf1lVXRi2FJccRsu_pNMeUISNNuB4xlNSXGR-ouKHLkGdIcQn2O_nqYEj4Yx9PyPPd7dP1Q_X45_739dVj1UvOctVYbVXbNSgRNXets6y3IIAxUdelEiChY9rVKGultNO8Z5dcdayzunMWxQk53-0tsl4XTNmMPvU4DBAwLsnUbfmUVErIgv7co0s3ojXTXM6eN-b9ZQU42wE-TuYlLnMoys00dqZVRhmmJGO1mawr5OoTkjOz9c5szTFbc8zOO_EGT7OMvQ |
| CODEN | PHMBA7 |
| CitedBy_id | crossref_primary_10_5534_wjmh_230050 crossref_primary_10_1016_j_ultrasmedbio_2022_12_005 crossref_primary_10_1016_j_brachy_2022_11_011 crossref_primary_10_1186_s41984_023_00213_0 crossref_primary_10_1088_1361_6560_aca069 crossref_primary_10_1002_ail2_101 crossref_primary_10_1007_s10278_023_00783_3 crossref_primary_10_1038_s41598_025_00309_7 crossref_primary_10_1016_j_zemedi_2022_10_005 crossref_primary_10_1007_s11548_025_03400_6 crossref_primary_10_1038_s41598_025_00966_8 crossref_primary_10_3390_jimaging8090252 crossref_primary_10_1016_j_patcog_2023_109925 crossref_primary_10_1088_1361_6560_acf5c5 crossref_primary_10_1016_j_eswa_2024_124279 crossref_primary_10_3390_diagnostics12123197 crossref_primary_10_1016_j_brachy_2023_04_003 crossref_primary_10_1109_TUFFC_2023_3255843 crossref_primary_10_1016_j_ultrasmedbio_2024_10_005 crossref_primary_10_1016_j_ejrad_2023_110928 crossref_primary_10_3390_cancers16193424 crossref_primary_10_1007_s13721_023_00412_7 crossref_primary_10_1016_j_semradonc_2022_06_008 crossref_primary_10_1007_s10489_023_04676_4 crossref_primary_10_3389_fphy_2024_1398393 crossref_primary_10_1016_j_bone_2023_116987 |
| ContentType | Journal Article |
| Copyright | 2022 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd Creative Commons Attribution license. |
| Copyright_xml | – notice: 2022 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd – notice: Creative Commons Attribution license. |
| DBID | O3W TSCCA CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1088/1361-6560/ac5a93 |
| DatabaseName | Institute of Physics Open Access Journal Titles IOPscience (Open Access) Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | 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: O3W name: Institute of Physics Open Access Journal Titles url: http://iopscience.iop.org/ sourceTypes: Enrichment Source Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Biology Physics |
| EISSN | 1361-6560 |
| ExternalDocumentID | 35240585 pmbac5a93 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: Ontario Institute for Cancer Research grantid: P.IT.033 funderid: https://doi.org/10.13039/501100004203 – fundername: Canadian Institutes of Health Research grantid: 374556 funderid: https://doi.org/10.13039/501100000024 – fundername: London Regional Cancer Program Catalyst Grant – fundername: Natural Sciences and Engineering Research Council of Canada funderid: https://doi.org/10.13039/501100000038 – fundername: CIHR |
| GroupedDBID | --- -DZ -~X 123 1JI 4.4 5B3 5RE 5VS 5ZH 7.M 7.Q AAGCD AAJIO AAJKP AATNI ABCXL ABHWH ABJNI ABLJU ABQJV ABVAM ACAFW ACGFS ACHIP AEFHF AENEX AFYNE AKPSB ALMA_UNASSIGNED_HOLDINGS AOAED ASPBG ATQHT AVWKF AZFZN CBCFC CEBXE CJUJL CRLBU CS3 DU5 EBS EDWGO EJD EMSAF EPQRW EQZZN F5P HAK IHE IJHAN IOP IZVLO KOT LAP M45 N5L N9A O3W P2P PJBAE R4D RIN RNS RO9 ROL RPA SY9 TN5 TSCCA UCJ W28 XPP CGR CUY CVF ECM EIF NPM 7X8 ADEQX AEINN |
| ID | FETCH-LOGICAL-c410t-5d8d76b5e4ee81f6fd0cda3a003226fd3a4ab08f2e42778f81c0917b0bd8bfde3 |
| IEDL.DBID | O3W |
| ISICitedReferencesCount | 28 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000774581100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1361-6560 |
| IngestDate | Fri Sep 05 07:58:07 EDT 2025 Wed Apr 16 06:21:20 EDT 2025 Wed Jun 07 11:18:59 EDT 2023 Wed Aug 21 03:34:57 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | deep learning image quality prostate cancer small dataset 3D ultrasound prostate segmentation biopsy brachytherapy |
| Language | English |
| License | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Creative Commons Attribution license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c410t-5d8d76b5e4ee81f6fd0cda3a003226fd3a4ab08f2e42778f81c0917b0bd8bfde3 |
| Notes | PMB-112471.R1 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://iopscience.iop.org/article/10.1088/1361-6560/ac5a93 |
| PMID | 35240585 |
| PQID | 2636147734 |
| PQPubID | 23479 |
| PageCount | 20 |
| ParticipantIDs | proquest_miscellaneous_2636147734 iop_journals_10_1088_1361_6560_ac5a93 pubmed_primary_35240585 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-04-07 |
| PublicationDateYYYYMMDD | 2022-04-07 |
| PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-07 day: 07 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Physics in medicine & biology |
| PublicationTitleAbbrev | PMB |
| PublicationTitleAlternate | Phys. Med. Biol |
| PublicationYear | 2022 |
| Publisher | IOP Publishing |
| Publisher_xml | – name: IOP Publishing |
| SSID | ssj0011824 |
| Score | 2.5370753 |
| Snippet | Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate... |
| SourceID | proquest pubmed iop |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| SubjectTerms | 3D ultrasound prostate segmentation biopsy brachytherapy Deep Learning Humans image quality Male Pelvis Prostate - diagnostic imaging prostate cancer Prostatic Neoplasms - diagnostic imaging small dataset Ultrasonography |
| Title | Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound |
| URI | https://iopscience.iop.org/article/10.1088/1361-6560/ac5a93 https://www.ncbi.nlm.nih.gov/pubmed/35240585 https://www.proquest.com/docview/2636147734 |
| Volume | 67 |
| WOSCitedRecordID | wos000774581100001&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 | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3da9UwFD9s8wNfps6v63RE0Ld1a5ukSdnTUIcP7roHP-5bSJpkXPC25bZXmOD_7kkTB4KC4EspIWnCSXLOj55zfgfgJeVoFDnHHaBFmbGK15n2uclMgdi0cdKaiaz683sxn8vFor7YgpPrXJiuT6r_CF8jUXAUYQqIk8cFrYoscMYc64brmm7DDSrRjONh_kC_XLsQEDiz5Jf80yi0JTjB33HlZF_O7v7Xyu7BboKV5DR2vQ9brt2DW7HQ5NUe3D5PLnRsnGI-m-EB_IjMxaTzJMSJDm4kw_K7OyTLFSoZEtMtrw6Jbm1qCv9rSdcS61xPUr2JyywYQkv0Zuwm-lfSh0QShLBkcJerlNrUkmVL6Buy-Tqu9RBKOT2ET2dvP75-l6VqDFnDinzMuJVWVIY75pwsfOVt3lhNNaoFhHDeUs20yaUvHSuFkF4WDWIRYXJjpfHW0Uew03atewIER3EZqpxw75mtpbSsrqzwVBeVbQSbwSsUsUq3aVCTo1xKFeSrgnxVlO8MyG_9-pVRlVBCIS5CTa9662fw4tcGK7w4wRuiW9dtBlVW-DUmBMXpHsedV31k-FCIShHISv70HxeyD3fKkBYRInrEM9gZ1xv3HG4238blsD6AbbGQ-JxfnB9MZ_QnBHnl4A |
| linkProvider | IOP Publishing |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3di9QwEB_01ONe_Dg_bv2MoG_X27ZJmvRRPBfFc70HP-4tJE1yLLht2XaFE_zfnTRREBQE30pomjCTzPzozPwG4Bnl6BQ5Rw3QosxYxetM-9xkpkBs2jhpzURW_elELJfy7Kw-TX1Op1qYrk-m_wgfI1FwFGFKiJPzglZFFjhj5rrhuqbz3vrLcCXwlIRj_Z5-_hVGQPDMUmzyTzPRn-Aif8eWk49Z3Pjv3d2E6wlekhfx9VtwybX7cC02nLzYh913KZSOg1PuZzPchu-RwZh0noR80cGNZFh9c4dktUZjQ2LZ5cUh0a1NQ-G_LelaYp3rSeo7cZ4Fh2iJ3o7dRANL-lBQglCWDO58nUqcWrJqCT0m2y_jRg-hpdMd-Lh49eHl6yx1ZcgaVuRjxq20ojLcMedk4Stv88ZqqtE8IJTzlmqmTS596VgphPSyaBCTCJMbK423jt6FnbZr3QEQnMVl6HbCvWe2ltKyurLCU11UthFsBs9RzCrdqkFNAXMpVZCxCjJWUcYzIL-916-NqoQSCvERWnyFKpjB059KVniBQlREt67bDqqs8GtMCIrL3YvaV31k-lB4pBDQSn7_HzfyBHZPjxfq5M3y7QPYK0OlREjyEQ9hZ9xs3SO42nwdV8Pm8XRMfwCPs-lU |
| 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=Effect+of+dataset+size%2C+image+quality%2C+and+image+type+on+deep+learning-based+automatic+prostate+segmentation+in+3D+ultrasound&rft.jtitle=Physics+in+medicine+%26+biology&rft.au=Orlando%2C+Nathan&rft.au=Gyacskov%2C+Igor&rft.au=Gillies%2C+Derek+J&rft.au=Guo%2C+Fumin&rft.date=2022-04-07&rft.pub=IOP+Publishing&rft.eissn=1361-6560&rft.volume=67&rft.issue=7&rft_id=info:doi/10.1088%2F1361-6560%2Fac5a93&rft.externalDocID=pmbac5a93 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1361-6560&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1361-6560&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1361-6560&client=summon |