Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

R Shijitha,P Karthigaikumar,A Stanly Paul

doi:10.32604/cmc.2022.020227

Abstract

Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction. Precise segmentation of brain hemorrhage is crucial, so an enhanced segmentation is carried out in this research work. The brain image of various patients has taken using an MRI scanner by the utilization of T1, T2, and FLAIR sequence. This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet (multires UNet) through morphological operations. It is hard to precisely segment the brain lesions to extract the existing region of stroke. This crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke lesions. The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95% compared with the existing segmentation techniques such as U-net++, ResNet, Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced outcome. The performance measure of the proposed methodology acquires an improved accuracy, precision ratio, sensitivity, and specificity rate of 0.07%, 0.04%, 0.04%, and 0.05% in comparison to U-net, ResNet, Multires UNET and 3D-ResU-Net techniques respectively.

Highlights

Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction
It is hard to precisely segment the brain lesions to extract the existing region of stroke. This crucial step is accomplished by this proposed morphological multiResU-Net (MMU-Net) methodology by precise segmentation of stroke lesions
The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95% compared with the existing segmentation techniques such as U-net++, ResNet, Multires UNET and 3D-ResU-Net and provides improved performance of 2D and 3D U-Net with an enhanced outcome

Summary

Introduction

Stroke is a crucial malady caused by irregular blood flow in a specific area of the brain which provokes damage to the brain cells and lacks delivering oxygen and nutrients to the brain tissues. Segmentation of brain lesions is a critical role in stroke detection. Reference [4] Segmentation and thorough examination of lesions in medical images provide valuable evidence for neuropathology diagnosis and are critical for clinical planning, disease prevention, and patient outcome prediction. Several lesional stroke segmentation approaches have been proposed, which have been classified as supervised [7] or unsupervised depending on whether prior knowledge is needed. Normalized segmentation results in the lesional stroke detection with limited accuracy, but the morphological segmentation results from the morphological operation in the feature of the brain lesions results with improved accuracy. A multi-resolution UNet-based method has been used to increase the performance of medical picture segmentation. Reference [14] 3D deep neural network for semantic segmentation of hemorrhage lesions has been evaluated in earlier approaches but lacks proper complete scanning is due to its downgraded efficiency.

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