Tumor segmentation in whole-slide histology images using deep learning

Abstract

The paper addresses the problem of segmentation of malignant tumors in large whole-slide histology images in the context of computer-assisted diagnosis of breast cancer. The method presented in this study is based on image classification procedure of norm/tumor type. The procedure calculates probability of belonging of each particular elementary image region of 256×256 pixels to the "tumor" class, which are isolated by corresponding sliding-window technique. The procedure capitalizes on convolutional neural networks and Deep Learning methods. The neural networks being employed were trained on a representative dataset of 600 000 fragments sampled from whole slide images and representing the morphological and colorimetric variability of two classes. The resultant probability maps were post-processed using conventional image processing algorithms to obtain the final binary masks of pathological regions. The proposed algorithm of segmentation of whole slide histological images can be used in computerized diagnosis of cancer for detection and segmentation of malignant tumors.

Keywords

• whole-slide histology images
• deep learning
• convolutional neural networks
• image segmentation
• computerized cancer diagnosis