Histopathological image analysis with connections to genomics
Chidester, Benjamin Wilson
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https://hdl.handle.net/2142/97764
Description
Title
Histopathological image analysis with connections to genomics
Author(s)
Chidester, Benjamin Wilson
Issue Date
2017-04-21
Director of Research (if dissertation) or Advisor (if thesis)
Do, Minh N.
Doctoral Committee Chair(s)
Do, Minh N.
Committee Member(s)
Ma, Jian
Bhargava, Rohit
Boppart, Stephen
Liang, Zhi-Pei
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Histopathological image processing
Genomics
Computational biology
Computational pathology
Signal processing
Image processing
Abstract
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.
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