Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Abstract

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. Such complex networks need large training datasets, a requirement which is challenging for medical image analysis. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image semantic segmentation, (2) prediction of the instance class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions at different perceptual levels, we propose to allow their interaction within the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise semantic segmentation and the instance class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate segmentation, even though the number of images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved outperformed or comparable performance in all the three challenges. Our code, typical data and trained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xuhuaren/TDSNet</uri> .