A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring.

ABSTRACT: One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan-Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan-Vese

SUBMITTER: Gunasekara SR 

PROVIDER: S-EPMC7948532 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature