Image processing on histopathological images of urothelial carcinoma – assessment of immune cells

Abstract

Bladder cancer is the 6th most common cancer in the world, where urothelial carcinoma is the most common one. Bladder cancer is one of the most economically expensive cancers to treat, as follow up is needed over a long period of time. Through extensive research, it has been indicated that the amount of tumor infiltrating lymphocytes(TIL) can have a positive impact on the relapse rate in conjunction with treatment.

This paper concentrates on image processing to identify, and analyze the amount of TIL cells in histological images of bladder tissue. The objective of this thesis is to locate all cells in a histological image, and to train a classifier to predict if a cell is a TIL or not. The end goal is to automatically determine the amount of TILs in an image which in turn can be used to predict the effectiveness of cancer treatment. A sub set of microscopic tissue samples has been derived from digitized samples, made available by Stavanger Universitetssykehus, to be able to analyze the quantitative performance of the proposed system.

Using a distance transform, in conjunction with pre-processing methods, to 93% of the cells in the histological images were found. A side effect was that there were wrongly located multiple cell centers for some cells, in addition to other non-cell objects in the histological images. Prediction of the located cells, using histogram features, was able to achieve 92% accuracy. Using local binary pattern features, the prediction accuracy was reduced to 73%. Synthetic over-sampling was introduced as the prediction showed a higher accuracy for correctly predicted non-TILs, but this proved to decrease the quantitative performance.