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

• URL: http://arxiv.org/abs/2012.11952v1
• Date: Tue, 22 Dec 2020 12:11:42 GMT
• Title: A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images
• Authors: Shanaka Ramesh Gunasekara, HNTK Kaldera, Maheshi B. Dissanayake
• Abstract summary: 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. 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. It was observed that the confidence level of our segmented output was in a similar range to that of experts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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