Automated Segmentation of Complex Biological Structures in Histopathology Images

Analysis of high-resolution histopathology whole slide images (WSIs) is a vital step in diagnosis and treatment of many diseases, including placenta-mediated diseases (PMDs) and respiratory illnesses. Currently, the most trusted approach is manual/semi-automated analysis of histopathology WSIs by an expert pathologist. This is problematic because high-resolution histopathology WSIs usually have a very large size (e.g., 80,000×80,000 pixels) and a large number of complex biological structures. As such, applying manual/semi-automated approaches to assess histopathology WSIs can be inefficient, expensive, and subject inter- and intra-rater variability. An alternative approach to manual and semi-automated approaches is to implement machine learning and image processing techniques to develop automated histopathology image analysis (AHIA) pipelines. A fundamental step in generating accurate AHIA approaches is semantic segmentation of biological structures in high-resolution histopathology images. In this thesis, our main objective is to develop accurate AHIA pipelines for semantic segmentation of complex biological structures in histopathology WSIs. We specifically focus on two histopathology applications: 1) segmentation of villi in histopathology WSIs of human placenta and 2) segmentation of complex biological structures of mouse lung tissue. Initially, we investigate the rule-based methods using conventional machine learning and image processing methods for segmentation of histopathology WSIs, developing two separate pipelines to address the challenges associated with each of our applications. We demonstrate that rule-based AHIA approaches show promising performance for analysis of histopathology WSIs in each of applications in comparison to manual assessment by expert pathologists and can be considered as a potential replacement for manual/semi-automated approaches. Then, we investigate deep learning methods in semantic segmentation of histopathology WSIs to further improve our developed rule-based approaches in terms of segmentation performance, generalizability, and training and testing speed. One of the bottlenecks in developing deep learning methods is the large size of histopathology WSIs, which requires implementing a patch-based approach to feed the images to deep learning models. We demonstrate that this bottleneck may limit the performance of the deep learning models due to three-fold trade-off between field-of-view, computational efficiency, and spatial resolution. As such, we propose a multi-resolution semantic segmentation pipeline to address this trade-off in AHIA using deep learning.