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%0 Thesis
%A Gupta, Laxmi
%T Interpretable image features and stain-independent machine learning methods for automated analysis of renal histopathology
%I Rheinisch-Westfälische Technische Hochschule Aachen
%V Dissertation
%C Aachen
%M RWTH-2023-08404
%P 1 Online-Ressource : Illustrationen
%D 2023
%Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University
%Z Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023, Kumulative Dissertation
%X Histological whole slide images are conventionally analyzed visually by clinical experts, which is a highly labor and time intensive procedure. However, analyzing the extensive information on such images in an automated way has been difficult so far due to the vast sizes of the images, tissue-, pathological-, staining-variations, and so on. These factors account for some of the most important limitations in histopathological analyses. In this work, the goal is to overcome some of these limitations by automating histopathological analyses with the following focus:1. To extract quantitative and interpretable features to expand prior biological knowledge, in an -omics like approach, “Pathomics”.2. To make automatic histopathological analyses stain-independent and exploit information contained in stains to improve automatic analyses. All methods in this work are based on renal images obtained from mice. For the first part, we developed a novel pipeline that extracts a comprehensive set of visual features as well as sub-visual features. Here, visual features are those which are detectable by a pathologist, and sub-visual features are those which are not discernible by human experts. A large set of features (intensity, textural, shape, morphological, color, and nuclei-related) were extracted from several renal compartments including glomerular tuft, Bowmann’s Capsule, tubule, interstitium, arterial blood vessels and their lumen. This approach, similar to Radiomics, is referred to as Pathomics in pathology. We defined feature selection methods to extract the most informative and discriminative features and performed statistical analyses to understand the relation of the extracted features, both individually, and in combinations, with tissue morphology and pathology. In the presented case-study, we highlight features that are affected in each compartment for experimental unilateral ureteral obstruction and their contralateral tissue for comparative analyses. In this way, prior biological knowledge is confirmed and presented in a quantitative way, alongside with novel findings. The proposed approach provides a quantitative, reproducible, and rater-independent characterization of whole slide images, e.g. for quantitatively assessing disease-specific changes in histopathology. To address the second goal, we developed Generative Adversarial Networks based methods which facilitate virtual stain translations. This makes it possible to perform stain independent analyses, which overcomes a major limitation in automatic histopathological analyses. In this work, we focus on utilizing the virtually stained images thus obtained to further improve the performance of deep learning algorithms. To this end, we introduced the idea of “image enrichment”: we merge virtually stained images with the original image to create a multi-channel image, which provides an “enriched” image with a higher information content. We prove the gain of information by showing that deep neural networks trained with the enriched images show higher segmentation accuracies.
%F PUB:(DE-HGF)11
%9 Dissertation / PhD Thesis
%R 10.18154/RWTH-2023-08404
%U https://publications.rwth-aachen.de/record/968038