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

CT脑组织图像分割对颅脑外伤的临床诊断与治疗具有重要辅助作用。基于此,研究引入基于深度学习的V-Net模型进行脑组织定位,同时引入注意力机制,以实现脑组织图像的精准分割。结果表明,研究所提分割模型的Dice指标最高达到99.81%。同时,该分割模型的精确率与召回率最高分别达到99.38%、99.84%。说明,研究所提算法具有显著的性能优势,且具有良好的实际应用效果,可为颅脑外伤的脑部诊断及治疗提供可靠的技术支持。

CT brain tissue image segmentation plays an important auxiliary role in the clinical diagnosis and treatment 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.