Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images.

Yanyan Pan,Ge Song,Jinsuo Yang,Huiping Zhang,Zhiguo Yang,Jing Guo,Jianbing Wang,Yuvaraja Teekaraman

doi:10.1155/2021/7598613

Abstract

This study aimed to explore the application value of multimodal magnetic resonance imaging (MRI) images based on the deep convolutional neural network (Conv.Net) in the diagnosis of strokes. Specifically, four automatic segmentation algorithms were proposed to segment multimodal MRI images of stroke patients. The segmentation effects were evaluated factoring into DICE, accuracy, sensitivity, and segmentation distance coefficient. It was found that although two-dimensional (2D) full convolutional neural network-based segmentation algorithm can locate and segment the lesion, its accuracy was low; the three-dimensional one exhibited higher accuracy, with various objective indicators improved, and the segmentation accuracy of the training set and the test set was 0.93 and 0.79, respectively, meeting the needs of automatic diagnosis. The asymmetric 3D residual U-Net network had good convergence and high segmentation accuracy, and the 3D deep residual network proposed on its basis had good segmentation coefficients, which can not only ensure segmentation accuracy but also avoid network degradation problems. In conclusion, the Conv.Net model can accurately segment the foci of patients with ischemic stroke and is suggested in clinic.

Highlights

Precision treatment of brain diseases has become a research hotspot
Each patient underwent seven magnetic resonance imaging (MRI) examinations in the acute phase, including T1c, T2, CBF, CBV, DWI, Tmax, and TTP. e registration and skull dissection were performed in each MRI mode, and the image resolution was 2 × 2 × 2 mm3. e MRI data of 30 patients were used as the training set, and 30 patients undergoing 7 MRI examinations were in the test set. e “penumbra” was marked manually by an expert based on the perfusion and diffusion images, as per the currently recognized linear threshold
It was noted that the segmentation results by the cascaded 3D deep residual network algorithm were highly consistent with the lesion labels drawn by the expert

Summary

Introduction

Precision treatment of brain diseases has become a research hotspot. Under normal circumstances, brain diseases generally fall into two categories, namely, acute brain diseases and chronic brain diseases [1]. e so-called acute brain disease refers to cerebrovascular diseases of a sudden onset, and stroke is a most typical one. E so-called acute brain disease refers to cerebrovascular diseases of a sudden onset, and stroke is a most typical one. Ischemic stroke refers to the gradual necrosis of brain cells arising from the lack of oxygen in the brain tissue due to insufficient blood supply, thereby forming an irreversible infarct core. If blood flow is restored in time, irreversible brain damage may be avoided. Is potentially reversible tissue area surrounding the core of ischemic damage is called the “ischemic penumbra.”. According to the onset time, an onset within 0–24 h is called the acute stroke, an onset within 24 h-2 w is called the subacute stroke, and an onset during a period over 2 w is called the chronic stroke. Patients with an acute stroke are manifested as focal neurological impairment, such as aphasia, hemianopia, and loss of sensation

Methods

Results

Discussion

Conclusion