Automated Segmentation of DCIS in Whole Slide Images

Nikhil Seth, Shazia Akbar, Sharon Nofech-Mozes, Sherine Salama, Anne L. Martel

Abstract: Segmentation of ducts in whole slide images is an important step needed to analyze ductal carcinoma in-situ (DCIS), an early form of breast cancer. Here, we train several U-Net architectures – deep convolutional neural networks designed to output probability maps – to segment DCIS in whole slide images and validate the optimal patch field of view necessary to achieve superior accuracy at the slide-level. We showed a U-Net trained at 5x achieved the best test results (DSC = 0.771, F1 = 0.601), implying the U-Net benefits from having wider contextual information. Our custom U-Net based architecture, trained to incorporate patches from all available resolutions, achieved test results of DSC = 0.759 (F1 = 0.682) showing improvement in the duct detecting capabilities of the model. Both architectures show comparable performance to a second expert annotator on an independent test set. This is preliminary work for a pipeline targeted at predicting recurrence risk in DCIS patients.