{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113936"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113936","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning-based tumor segmentation using metabolic imaging features","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","abstract_has_math":false,"creators":["Liang, Yucheng"],"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":2022,"date_issued":"2022-04-29T21:35:55Z","date_published":"2022-04-29T21:35:55Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Engineering"],"languages":["en","eng"],"rights":["Copyright 2021 Yucheng Liang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113936","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":["Liang, Yucheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:35:55Z","2021-12","2021-12-10"]},{"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":["Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Yucheng Liang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113936"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Yucheng Liang, accepted the attached license on 2021-12-10 at 13:49.","The student, Yucheng Liang, submitted this Thesis for approval on 2021-12-10 at 14:14.","This Thesis was approved for publication on 2021-12-10 at 15:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17450 on 2022-04-06 at 17:11:20","Made available in DSpace on 2022-04-29T21:35:55Z (GMT). No. of bitstreams: 2 LIANG-THESIS-2021.pdf: 7261561 bytes, checksum: 5e12114c7316cb33cd742087bb0c52be (MD5) LICENSE.txt: 4210 bytes, checksum: 76162280e78014acc3bc3d021ccafdf6 (MD5) Previous issue date: 2021-12-10","The ultimate objective of brain tumor imaging is to distill patient-specific knowledge that guides therapy planning and medical care. Magnetic resonance imaging (MRI) is a prevailing technique to visualize tumors due to its excellent contrast of soft tissue and non-invasiveness. Decades of research have helped brain tumor segmentation performance dramatically. However, precise segmentation is still considered hard partly due to the limitation in resolution, signal-to-noise ratio, and possible artifacts. While some tumors are easier to delineate, more infiltrating ones like gliomas have ragged and obscure boundaries that are harder to define. In recognition of this hardship, researchers have started exploring the use of Proton Magnetic Resonance Spectroscopic Imaging (MRSI) for better tumor prognosis, diagnosis, and characterization. MRSI investigates the spatial distribution of metabolic changes by leveraging its unique temporal information. The wealth of this spectroscopic information is beneficial in classifying tumor subregions and aiding ongoing research investigations in tumor heterogeneity and related topics. Several studies have reported an increase in choline-containing compounds level and a reduced N-acetyl-aspartate level in brain tumors. Spectroscopic techniques can pick up these metabolic changes, and they might be the missing pieces of better MRI-based tumor segmentation solutions. This study shows a successful application of deep learning and MRSI to identify tumor and edema regions of human brains with glioblastomas. The deep learning framework of choice is nnU-Net. Most specialized solutions in applying deep neural models in the medical image domain depend on dataset properties and hardware constraints. nnU-Net is a framework that automatically adapts itself to various medical image segmentation tasks. Therefore, it ensures a fair comparison of experiments. This work shows an improved segmentation result after incorporating high-resolution metabolite maps derived from MRSI data acquired by the SPICE sequence. The high resolution, rapidness, and near whole-brain performance of SPICE should assist radiologists and oncologists in delimiting the pathological area better and providing more appropriate medical help."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning-based tumor segmentation using metabolic imaging features"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei"],"dc:creator":["Liang, Yucheng"],"dc:date":["2022-04-29T21:35:55Z","2021-12","2021-12-10"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms","The student, Yucheng Liang, accepted the attached license on 2021-12-10 at 13:49.","The student, Yucheng Liang, submitted this Thesis for approval on 2021-12-10 at 14:14.","This Thesis was approved for publication on 2021-12-10 at 15:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17450 on 2022-04-06 at 17:11:20","Made available in DSpace on 2022-04-29T21:35:55Z (GMT). No. of bitstreams: 2 LIANG-THESIS-2021.pdf: 7261561 bytes, checksum: 5e12114c7316cb33cd742087bb0c52be (MD5) LICENSE.txt: 4210 bytes, checksum: 76162280e78014acc3bc3d021ccafdf6 (MD5) Previous issue date: 2021-12-10","The ultimate objective of brain tumor imaging is to distill patient-specific knowledge that guides therapy planning and medical care. Magnetic resonance imaging (MRI) is a prevailing technique to visualize tumors due to its excellent contrast of soft tissue and non-invasiveness. Decades of research have helped brain tumor segmentation performance dramatically. However, precise segmentation is still considered hard partly due to the limitation in resolution, signal-to-noise ratio, and possible artifacts. While some tumors are easier to delineate, more infiltrating ones like gliomas have ragged and obscure boundaries that are harder to define. In recognition of this hardship, researchers have started exploring the use of Proton Magnetic Resonance Spectroscopic Imaging (MRSI) for better tumor prognosis, diagnosis, and characterization. MRSI investigates the spatial distribution of metabolic changes by leveraging its unique temporal information. The wealth of this spectroscopic information is beneficial in classifying tumor subregions and aiding ongoing research investigations in tumor heterogeneity and related topics. Several studies have reported an increase in choline-containing compounds level and a reduced N-acetyl-aspartate level in brain tumors. Spectroscopic techniques can pick up these metabolic changes, and they might be the missing pieces of better MRI-based tumor segmentation solutions. This study shows a successful application of deep learning and MRSI to identify tumor and edema regions of human brains with glioblastomas. The deep learning framework of choice is nnU-Net. Most specialized solutions in applying deep neural models in the medical image domain depend on dataset properties and hardware constraints. nnU-Net is a framework that automatically adapts itself to various medical image segmentation tasks. Therefore, it ensures a fair comparison of experiments. This work shows an improved segmentation result after incorporating high-resolution metabolite maps derived from MRSI data acquired by the SPICE sequence. The high resolution, rapidness, and near whole-brain performance of SPICE should assist radiologists and oncologists in delimiting the pathological area better and providing more appropriate medical help."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113936"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Yucheng Liang"],"dc:subject":["Engineering"],"dc:title":["Learning-based tumor segmentation using metabolic imaging features"],"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":"2026-07-22T22:24:53Z"}