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oai:www.ideals.illinois.edu:2142/97764 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Do, Minh N. Do, Minh N. Ma, Jian Bhargava, Rohit Boppart, Stephen Liang, Zhi-Pei Chidester, Benjamin Wilson 2017-08-10T20:33:19Z 2017-08-10T20:33:19Z 2019-08-11T09:15:28Z 2017-04-21 2017-05 The fields of imaging and genomics in cancer research have been mostly studied independently, but recently available datasets have made investigation into the synergy of these two fields possible. This work demonstrates the efficacy of computational histopathological image analysis to extract meaningful quantitative nuclear and cellular features from hematoxylin and eosin stained images that have meaningful connections to genomic data. Additionally, with the advent of whole slide images, significantly more data representing the variation in nuclear characteristics and tumor heterogeneity is available, which can aid in developing new analytical tools, such as the proposed convolutional neural network for nuclear segmentation, which produces state-of-the-art segmentation results on challenging cases seen in normal pathology. This robust segmentation tool is essential for capturing reliable features for computational pathology. Additionally, whole slide images capture rich spatial information about tumors, which presents a challenge, but also an opportunity for the development of new image processing tools to capture this spatial information, which could be considered for future work. Other histopathological image modalities and relevant machine learning tools are also considered for elucidating cellular processes of cancer. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-05-01 The student, Benjamin Chidester, accepted the attached license on 2017-04-21 at 09:33. The student, Benjamin Chidester, submitted this Dissertation for approval on 2017-04-21 at 09:47. This Dissertation was approved for publication on 2017-04-21 at 12:13. DSpace SAF Submission Ingestion Package generated from Vireo submission #10957 on 2017-08-10 at 15:06:42 Made available in DSpace on 2017-08-10T20:33:19Z (GMT). No. of bitstreams: 2 CHIDESTER-DISSERTATION-2017.pdf: 19370661 bytes, checksum: 8ff22f83cecbab28e658139c093b1d41 (MD5) LICENSE.txt: 4215 bytes, checksum: 12d52fd9a9834f47d5d4c0fd1a16ba4c (MD5) Previous issue date: 2017-04-21 Embargo set by: Colleen Fallaw for item 102817 Lift date: 2019-08-10T21:27:21Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 102817 on 2019-08-11T09:15:28Z. application/pdf http://hdl.handle.net/2142/97764 en Copyright 2017 Benjamin Chidester Histopathological image processing Genomics Computational biology Computational pathology Signal processing Image processing Histopathological image analysis with connections to genomics text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Dissertation Ph.D.