{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110723"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110723","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Brain tumor segmentation with multimodal magnetic resonance imaging","abstract":"Made available in DSpace on 2021-09-17T02:34:43Z (GMT). No. of bitstreams: 2 WANG-THESIS-2021.pdf: 1892077 bytes, checksum: fe6bc0de2dfc1d1451e42c22f85e49e1 (MD5) LICENSE.txt: 4204 bytes, checksum: 4e619a5d0afe426aea11320e559f16a5 (MD5) Previous issue date: 2021-04-26","abstract_html":"Made available in DSpace on 2021-09-17T02:34:43Z (GMT). No. of bitstreams: 2 WANG-THESIS-2021.pdf: 1892077 bytes, checksum: fe6bc0de2dfc1d1451e42c22f85e49e1 (MD5) LICENSE.txt: 4204 bytes, checksum: 4e619a5d0afe426aea11320e559f16a5 (MD5) Previous issue date: 2021-04-26","abstract_has_math":false,"creators":["Wang, Bo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Liang, Zhi-Pei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:43Z","date_published":"2021-09-17T02:34:43Z","updated_at":"2026-07-22T22:24:52Z","subjects":["MRI","image segmentation","brain tumor","CNN","deep-learning"],"languages":["en"],"rights":["Copyright 2021 Bo Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110723","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Zhi-Pei"]},{"key":"dc:creator","label":"Author","values":["Wang, Bo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:43Z","2023-09-17T02:34:57Z","2021-04-26","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["MRI","image segmentation","brain tumor","CNN","deep-learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Bo Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110723"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Made available in DSpace on 2021-09-17T02:34:43Z (GMT). No. of bitstreams: 2 WANG-THESIS-2021.pdf: 1892077 bytes, checksum: fe6bc0de2dfc1d1451e42c22f85e49e1 (MD5) LICENSE.txt: 4204 bytes, checksum: 4e619a5d0afe426aea11320e559f16a5 (MD5) Previous issue date: 2021-04-26","A brain tumor is an abnormal cell population that occurs in the brain. Identifying the abnormal region is one of the significant steps for patients with a brain tumor. However, manual labeling is subjective and time-consuming. For the patients to get an accurate diagnosis and timely surgery, an accurate and efficient automatic brain tumor detection and segmentation algorithm is necessary. For medical images, with limited data and a high requirement for accuracy, the traditional learning algorithms are currently insufficient. We follow the idea of most state-of-the-art approaches with multimodal brain tumor image segmentation, taking advantage of different modalities from magnetic resonance imaging (MRI) which can provide different texture features of the same brain image sample. However, most open-source brain tumor MRI images are recorded by modalities T1, T2, and FLAIR. In this thesis, we include a new modality of MRI imaging into existing modalities: Magnetization-Prepared Rapid Gradient Echo (MPRAGE). By including additional MPRAGE modality and applying corresponding input-level multimodal segmentation fusion strategy, we can enrich the image information for each single case and provide more intensity or texture information on specific regions of abnormal tissues to corresponding computing systems. With a comprehensive comparison of different segmentation models and different modalities usage, we can prove the improvement in efficiency and performance brought by MPRAGE modality in different multimodal brain tumor segmentation approaches.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Bo Wang, accepted the attached license on 2021-04-21 at 19:39.","The student, Bo Wang, submitted this Thesis for approval on 2021-04-21 at 19:49.","This Thesis was approved for publication on 2021-04-26 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16487 on 2021-09-16 at 17:04:32","Embargo set by: Seth Robbins for item 118566 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Brain tumor segmentation with multimodal magnetic resonance imaging"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei"],"dc:creator":["Wang, Bo"],"dc:date":["2021-09-17T02:34:43Z","2023-09-17T02:34:57Z","2021-04-26","2021-05"],"dc:description":["Made available in DSpace on 2021-09-17T02:34:43Z (GMT). No. of bitstreams: 2 WANG-THESIS-2021.pdf: 1892077 bytes, checksum: fe6bc0de2dfc1d1451e42c22f85e49e1 (MD5) LICENSE.txt: 4204 bytes, checksum: 4e619a5d0afe426aea11320e559f16a5 (MD5) Previous issue date: 2021-04-26","A brain tumor is an abnormal cell population that occurs in the brain. Identifying the abnormal region is one of the significant steps for patients with a brain tumor. However, manual labeling is subjective and time-consuming. For the patients to get an accurate diagnosis and timely surgery, an accurate and efficient automatic brain tumor detection and segmentation algorithm is necessary. For medical images, with limited data and a high requirement for accuracy, the traditional learning algorithms are currently insufficient. We follow the idea of most state-of-the-art approaches with multimodal brain tumor image segmentation, taking advantage of different modalities from magnetic resonance imaging (MRI) which can provide different texture features of the same brain image sample. However, most open-source brain tumor MRI images are recorded by modalities T1, T2, and FLAIR. In this thesis, we include a new modality of MRI imaging into existing modalities: Magnetization-Prepared Rapid Gradient Echo (MPRAGE). By including additional MPRAGE modality and applying corresponding input-level multimodal segmentation fusion strategy, we can enrich the image information for each single case and provide more intensity or texture information on specific regions of abnormal tissues to corresponding computing systems. With a comprehensive comparison of different segmentation models and different modalities usage, we can prove the improvement in efficiency and performance brought by MPRAGE modality in different multimodal brain tumor segmentation approaches.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Bo Wang, accepted the attached license on 2021-04-21 at 19:39.","The student, Bo Wang, submitted this Thesis for approval on 2021-04-21 at 19:49.","This Thesis was approved for publication on 2021-04-26 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16487 on 2021-09-16 at 17:04:32","Embargo set by: Seth Robbins for item 118566 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110723"],"dc:language":["en"],"dc:rights":["Copyright 2021 Bo Wang"],"dc:subject":["MRI","image segmentation","brain tumor","CNN","deep-learning"],"dc:title":["Brain tumor segmentation with multimodal magnetic resonance imaging"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:52Z"}