Automated Grading of Bladder Cancer using Deep Learning
Urothelial carcinoma is the most common type of bladder cancer and is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on the histological grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra- and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under- or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists.
With the introduction of digital pathology, large amounts of data have been made available in the form of digital histological whole-slide images (WSI). However, despite the massive amount of data, annotations for the given data are lacking. Another potential problem is that the tissue samples of urothelial carcinoma contain a mixture of damaged tissue, blood, stroma, muscle, and urothelium, where it is mainly the urothelium tissue that is diagnostically relevant for grading.
A method for tissue segmentation is investigated, where the aim is to segment WSIs into the six tissue classes: urothelium, stroma, muscle, damaged tissue, blood, and background. Several methods based on convolutional neural networks (CNN) for tile-wise classification are proposed. Both single-scale and multiscale models were explored to see if including more magnification levels would improve the performance. Different techniques, such as unsupervised learning, semi-supervised learning, and domain adaptation techniques, are explored to mitigate the challenge of missing large quantities of annotated data.
It is necessary to extract tiles from the WSI since it is intractable to process the entire WSI at full resolution at once. We have proposed a method to parameterize and automate the task of extracting tiles from different scales with a region of interest (ROI) defined at one of the scales. The method is reproducible and easy to describe by reporting the parameters.
A pipeline for automated diagnostic grading is proposed, called TRIgrade. First, the tissue segmentation method is utilized to find the diagnostically relevant urothelium tissue. Then, the parameterized tile extraction method is used to extract tiles from the urothelium regions at three magnification levels from 300 WSIs. The extracted tiles form the training, validation, and test data used to train and test a diagnostic model. The final system outputs a segmented tissue image showing all the tissue regions in the WSI, a WHO grade heatmap indicating low- and high-grade carcinoma regions, and finally, a slide-level WHO grade prediction. The proposed TRIgrade pipeline correctly graded 45 of 50 WSIs, achieving an accuracy of 90%.
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