Multiclass Semantic Segmentation of Immunostained Breast Cancer Tissue with a Deep-Learning Approach

This paper describes a multiclass semantic segmentation of breast cancer images into the following classes: Tumour, Stroma, Inflammatory, Necrosis and Other. The images were stained with Haematoxilin and Eosin and acquired from the Cancer Genome Atlas through the Breast Cancer Semantic Segmentation Challenge. Over 12,000 patches of data and classes were generated from training images with the use of data augmentation.The segmentation was obtained with a U-Net architecture for which the hyperparameters were explored systematically. Optimal values were obtained with batch size = 8, Loss function Adam and 50 epochs, which took over 50 hours to train. Due to this fact and limitations in time, the rest of the parameters were explored with 10 epochs and we speculate that values would increase if 50 epochs would be used. The trained U-Net was applied to unseen images, per-patch and the following metrics were obtained from full scale WSI; Accuracy, Mean Area Under the Curve and Dice Index. No post-processing was applied. The resulting segmentations outperformed the baseline in terms of accuracy for some tissues; Tumour from 0.804 to 0.913, Inflammatory from 0.743 to 0.8364. The data is available from the Grand Challenges website (https://bcsegmentation.grand-challenge.org/) and the code is available from the following GitHub repository (https://github.com/mauOrtRuiz/Breast_Cancer_Sem_Seg).