Multi-Modal Semantic Segmentation Model using Encoder Based Link-Net Architecture for BraTS 2020 Challenge

Abstract

Glioma, which is a malignant tumor, is present in the glial tissue region of the human brain. Segmentation of such tumor cells in the brain region is still challenging and needs experts. Because of the overlap between the intensity distributions of tissue with edema, non-edema, and enhancing features, the segmentation process is a significant challenge for neurosurgeons and radiologists. As per the current state of the art in medicine and surgery, artificial intelligence is gaining attention in effective detection and segmentation in the area of medical diagnosis. In MICCAI 2020, the authors prepare an algorithm for the semantic segmentation of brain tumors from multimodal MRI images for further treatments such as observing treatment, monitoring recovery, and evaluating the effects of the treatment on patients. This paper's objective is to develop an efficient deep learning model which performs semantic segmentation using a multi-modal modified Link-Net model which uses Squeeze and Excitation ResNet152 model is used as a backbone for the segmentation. A model developed by Manipal Hospital in Bangalore is compared with the traditional state-of-the-art models, and its accuracy is verified by neurosurgeons there. This model imbibes the multi-modal MRI dataset which includes T1 weighted images, Flair images, and T2-weighted MRI images of the human brain, model perform comparably well, which shows that our model is robust for tumor segmentation. The accuracy of this model is 99.2.