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

Structural reliability and its sensitivity analysis based on the saddlepoint approximation-line sampling method by dichotomy of golden section

In order to solve the structural reliability and its sensitivity of the implicit nonlinear performance function (PF)  the advantages of the saddlepoint approximation (SA) and line sampling (LS) are merged. Also, the merits of dichotomy and the solution efficiency of the golden section method are combined to propose the saddlepoint approximation-line sampling (SA-LS) method based on the dichotomy of the golden section point. This is complicated and changeable in the non normal variable space, which is a very hot issue of the present international study.  For each sample, it is quick to find its zeropoint in PF along the important line sampling direction by the  previously mentioned dichotomy so that the structural failure probability can be transformed into the mean of a series linear PFs failure probability, and the reliability sensitivity is just the derivative or partial one of the probability with respect to the relational variables. Examples show that the SA-LS method based on the dichotomy of the golden section point is of high precision and fast velocity in analyzing the structural reliability and sensitivity of the implicit nonlinear PF that are complicated and changeable in the non-normal variable space