生命科学仪器2024，Vol.22Issue(1)：20-22,25,4.DOI:10.11967/2024220206
基于注意力机制和深度学习的颅脑外伤患者CT图像分割
CTImage Segmentation of Traumatic Brain Injury Patients Based on Attention Mechanism and Deep Learning
摘要
Abstract
CT brain tissue image segmentation plays an important auxiliary role in the clinical diagnosis and treat-ment of patients with craniocerebral trauma.Based on this,the study introduces a deep-learning-based V-Net model for brain tissue positioning,and also introduces an attention mechanism to realize accurate segmentation of brain tissue images.The results show that the Dice index of the segmentation model reached 99.81％.Meanwhile,the highest precision rate and recall rate of the segmentation model reached 99.38％and 99.84％,respectively.It shows that the proposed algorithm has significant performance advantages and good practical application effect,and provides reliable technical support for brain diagnosis and treatment of patients with brain trauma.关键词
V-Net/CT图像分割/注意力机制/颅脑外伤/脑组织Key words
V-Net/CT image segmentation/Attention mechanism/Craniocerebral injury/Brain tissue分类
医药卫生引用本文复制引用
尹红云,张丽娜,王佳明,周秀珍..基于注意力机制和深度学习的颅脑外伤患者CT图像分割[J].生命科学仪器,2024,22(1):20-22,25,4.基金项目
康复沙龙对脑出血患者术后运动功能、心理弹性及认知功能的影响,编号XJDX1711-2208 （）
