A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring

TL;DR

This paper introduces a three-stage deep learning and active contour system for accurate MRI brain tumor localization and segmentation, achieving high reliability and precision in delineating tumor boundaries.

Contribution

It presents a novel integrated approach combining CNN, R-CNN, and Chan-Vese active contour algorithms for improved tumor segmentation accuracy.

Findings

Average Dice score of 0.92 indicating high segmentation accuracy

High reliability demonstrated with RI of 0.9936 and low GCE of 0.004

Effective tumor boundary detection validated by multiple metrics

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) andsecond 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 concentratedtumor boundary is contoured for the segmentation process by using the Chan-Vesesegmentation 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, Chan- Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score,Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean absolute error (MAE), and Peak Signal to Noise Ratio (PSNR) werecalculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with average dice score of 0.92, (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076 and MAE of 52.946), pointing to high reliability of the proposed architecture.