A hybrid approach for mammary gland segmentation on CT images by embedding visual explanations from a deep learning classifier into a Bayesian inference

Abstract

We propose an approach for the automatic segmentation of mammary gland regions on 3D CT images, aiming to accomplish breast cancer risk assessment through CT scans acquired in clinical medicine for various diagnostic purposes. The proposed approach uses a hybrid method that embeds a deep-learning-based attention mechanism as a module into a conventional framework, which originally uses a probabilistic atlas to accomplish Bayesian inference to estimate the pixelwise probability of mammary gland regions on CT images. In this work, we replace both the construction and application of a probabilistic atlas, which is time-consuming and complicated to realize, by a visual explanation from the attention mechanism of a classifier learned through weak supervision. In the experiments, we applied the proposed approach to the segmentation of mammary gland regions based on 174 torso CT scans and evaluated its performance by comparing the segmentation results to human sketches on 14 CT cases. The experimental results showed that the attention maps of the classifier successfully focused on the mammary gland regions on the CT images and could replace the atlas for supporting mammary gland segmentation. The preliminary results on 14 test CT scans showed that the mammary gland regions were segmented successfully with a mean value of 50.6% on the Dice similarity coefficient against the human sketches. We confirmed that the proposed approach, combining deep learning and conventional methods, shows a higher computing efficiency, much better robustness, and easier implementation than our previous approach based on a probabilistic atlas.