A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images

TL;DR

This study explores the feasibility of using deep learning models, including CNNs and Faster R-CNN, for brain tumor segmentation in MRI images, evaluating their accuracy and clinical relevance.

Contribution

It introduces a combined deep learning approach for tumor classification, localization, and segmentation, and assesses its performance against expert evaluations.

Findings

High accuracy and confidence levels comparable to medical experts

Effective tumor localization and segmentation demonstrated

Model's clinical applicability supported by subjective assessments

Abstract

Deep learning algorithms have accounted for the rapid acceleration of research in artificial intelligence in medical image analysis, interpretation, and segmentation with many potential applications across various sub disciplines in medicine. However, only limited number of research which investigates these application scenarios, are deployed into the clinical sector for the evaluation of the real requirement and the practical challenges of the model deployment. In this research, a deep convolutional neural network (CNN) based classification network and Faster RCNN based localization network were developed for brain tumor MR image classification and tumor localization. A typical edge detection algorithm called Prewitt was used for tumor segmentation task, based on the output of the tumor localization. Overall performance of the proposed tumor segmentation architecture, was analyzed using objective quality parameters including Accuracy, Boundary Displacement Error (BDE), Dice score and confidence interval. A subjective quality assessment of the model was conducted based on the Double Stimulus Impairment Scale (DSIS) protocol using the input of medical expertise. It was observed that the confidence level of our segmented output was in a similar range to that of experts. Also, the Neurologists have rated the output of our model as highly accurate segmentation.