Abstract
The purpose of this paper is to explore the impact of magnetic resonance imaging (MRI) image features based on convolutional neural network (CNN) algorithm and conditional random field on the diagnosis and mental state of patients with severe stroke. 208 patients with severe stroke who all received MRI examination were recruited as the research objects. According to cerebral small vascular disease (CSVD) score, the patients were divided into CSVD 0∼4 groups. The patients who completed the three-month follow-up were classified into cognitive impairment group (124 cases) and the noncognitive impairment group (84 cases) according to the cut-off point of the Montreal cognitive assessment (MOCA) scale score of 26. A novel image segmentation algorithm was proposed based on U-shaped fully CNN (U-Net) and conditional random field, which was compared with the fully CNN (FCN) algorithm and U-Net algorithm, and was applied to the MRI segmentation training of patients with severe stroke. It was found that the average symmetric surface distance (ASSD) (3.13 ± 1.35), Hoffman distance (HD) (28.71 ± 9.05), Dice coefficient (0.78 ± 1.35), accuracy (0.74 ± 0.11), and sensitivity (0.85 ± 0.13) of the proposed algorithm were superior to those of FCN algorithm and U-Net algorithm. There were significant differences in the MOCA scores among the five groups of patients from CSVD 0 to CSVD 4 in the three time periods (0, 1, and 3 months) (P < 0.05). Differences in cerebral microhemorrhage (CMB), perivascular space (PVS), and number of cavities, Fazekas, and total CSVD scores between the two groups were significant (P < 0.05). Multivariate regression found that the number of PVS, white matter hyperintensity (WMH) Fazekas, and total CSVD score were independent factors of cognitive impairment. In short, MRI images based on deep learning image segmentation algorithm had good application value for clinical diagnosis and treatment of stroke and can effectively improve the detection effect of brain domain characteristics and psychological state of patients after stroke.
Highlights
Severe stroke is a cerebrovascular disease with high morbidity, disability, and mortality
Inclusion criteria were as follows: (i) the definition of severe stroke being 21 points ≤ National Institutes of Health Stroke Scale (NIHSS) scores ≤ 42 points according to the diagnostic criteria and confirmed by imaging examination as cerebral infarction [10]; (ii) age ≥18 years; (iii) those who can cooperate with medical staff to perform scale scoring and magnetic resonance imaging (MRI) examination; (iv) the basic information being complete, and the most important thing was that the patient was willing to cooperate with the follow-up; and (v) patients who signed the informed consent
According to the general structure, the segmentation effect of fully CNN (FCN) algorithm and U-Net algorithm was not outstanding, while the segmentation result based on U-Net and conditional random field algorithms was relatively accurate and fine
Summary
Severe stroke is a cerebrovascular disease with high morbidity, disability, and mortality. Erefore, it is very necessary to propose a high-efficiency and high-accuracy image segmentation algorithm. Convolutional neural network (CNN) algorithms have been widely used in medical image processing and lesion segmentation and have achieved remarkable results. Can they automatically extract the features in the data and they have objectivity and Contrast Media & Molecular Imaging efficiency compared with machine learning algorithms. To make better use of the deep information of the three-dimensional image, the CNN can be adopted to propose an algorithm that can quickly and accurately segment the MRI images of patients with severe stroke, which will be helpful for medical workers to diagnose and treat patients with severe stroke early
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