Diagnosis and Treatment Effect of Convolutional Neural Network-Based Magnetic Resonance Image Features on Severe Stroke and Mental State
| Autor: | Lihong Han, Li Liu, Yankun Hao, Lan Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male medicine.medical_specialty Multivariate statistics Article Subject 030204 cardiovascular system & hematology Convolutional neural network 03 medical and health sciences 0302 clinical medicine Sørensen–Dice coefficient Medical technology Image Processing Computer-Assisted Medicine Humans Radiology Nuclear Medicine and imaging Cognitive Dysfunction R855-855.5 Stroke Aged Aged 80 and over medicine.diagnostic_test business.industry Deep learning Montreal Cognitive Assessment Magnetic resonance imaging Middle Aged medicine.disease Prognosis Magnetic Resonance Imaging Test score Case-Control Studies Cerebral Small Vessel Diseases Female Artificial intelligence Radiology Neural Networks Computer business 030217 neurology & neurosurgery Algorithms Follow-Up Studies Research Article |
| Zdroj: | Contrast Media & Molecular Imaging Contrast Media & Molecular Imaging, Vol 2021 (2021) |
| ISSN: | 1555-4317 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|