Computational Methods for Histopathological Whole Slide Image Analysis of Osteosarcoma




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Computational image analysis methods have been successfully implemented in many tumor studies to assist pathologists and medical professionals in making informed decisions. Osteosarcoma is one of the most common types of bone cancer in children. Currently, to estimate a patient‘s cancer treatment response, pathologists manually evaluate Hematoxylin and Eosin (H&E) stained glass-slides. The slides are carefully prepared after a surgical resection, to calculate the percentage of tumor necrosis, a useful biomarker. This process is very time consuming and is subject to observer bias, which could impact subsequent treatment procedures. Digital image analysis automates this process, saves time and provides a more accurate evaluation. However, the size and format of the digital slide images in conjunction with the heterogeneity of the Osteosarcoma tissue regions makes the analysis a challenging task.

This research on Osteosarcoma focuses on developing image-analysis and machine-learning techniques to successfully predict tumor necrosis in histopathology image datasets (digitized glass-slides). The methods use whole slide images (WSIs) – high-resolution images consisting of more than 109 pixels, supporting up to 40X magnification. A comprehensive analysis is carried out for efficient necrosis identification by (1) using image processing methods to generate features, (2) performing comparative evaluation of feature sets, (3) identifying best automated learner, (4) comparative evaluation of classification approaches, and (5) testing the impact of extended feature set on learner accuracy. Image-tiles at a suitable magnification are generated from the WSIs and are normalized to remove color variations. They are segmented to compute color, shape, density and texture features. The features are grouped into two categories, namely, (1) expert-guided, and (2) automated-tool generated. Expert-guided features represent the properties pathologists observe while evaluating glass slides, and automated-tool generated features represent mainly texture-based properties. A comparative evaluation is performed to understand the significance of each feature-category. Both groups of features are combined and used as input-set to train and validate 13 machine-learning models. The best learner was a Support Vector Machine, which was used to perform comparative evaluation between a three-class and a two-class classification problem. An extended feature set is also generated by isolating sub-components of tissues from image-tiles and computing texture properties. A data-visualization step combines the results of classification into a tumor-prediction map which computes the percentage of tumor necrosis in a WSI.

The results from the above steps lead to the design of Necrosis Detection and Analysis Software. The tool is intended to perform an end-to-end image analysis of Osteosarcoma WSI images and is to be used by pathologists in a clinical setting. Two more applications have been created as part of this research - an image-tile annotation software, and a gross-image annotation software, which help pathologists in creating datasets for automated-learners, and gross-map area-computations, respectively.

The novel contributions of this research include, (1) building an automated image-analysis pipeline for Osteosarcoma, (2) creation of tumor-prediction maps from image-tiles, (3) design of an end-to-end necrosis detection tool, and (4) image-tile annotation and gross-image area-computation tools. The outcomes of this research will play a vital role in building novel, automated methods for Osteosarcoma and save valuable time of pathologists by reducing the time-consuming tumor necrosis estimation process.



Osteosarcoma—Imaging, Machine learning, Imaging systems in medicine, Tumors—Photographic measurements, Support vector machines, Necrosis


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