Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

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. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the 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 of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the 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 top-tier performance in all three challenges.Comment: IEEE Transactions on Medical Imagin

Overview of convolutional neural networks architectures for brain tumor segmentation

Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset