摘要
目的腰椎间盘突出症(lumbar disc herniation,LDH)是脊柱退行性疾病的常见类型,可导致腰痛和下肢神经症状。MRI图像在诊断中至关重要,但存在依赖经验和缺乏标准化机制的问题。研究旨在开发基于卷积块注意力模块-视觉目标检测算法(convolutional block attention module-you only look once,CBAM-YOLO)的深度学习模型,以辅助自动诊断LDH MRI图像,从而提高诊断的准确性和效率。方法研究采纳了一个包含643例LDH患者MRI图像数据的公开数据集,每张图像均详细标注了包括椎间盘等在内的多种结构信息,并提出了一种新型模型CBAM-YOLO,它是在原有YOLO-v8模型的基础上,通过嵌入卷积注意力模块CBAM进行改良。此改进有助于模型更精确地识别突出椎间盘的特征位置及空间分布信息。为了充分验证模型的性能,研究使用CBAM-YOLO模型对经过数据增强的训练集进行了系统训练,共进行了100个训练周期。在模型评估环节,研究采用了Precision、Recall、F1-Score、Accuracy以及mAP等多个评价指标,以全面而严谨地评估模型的性能表现。结果CBAM-YOLO模型在诊断腰椎间盘突出症MRI影像方面展现出了卓越的性能。相较于原YOLO-v8模型,研究所构建的CBAM-YOLO模型在Precision达到了89.9%,Recall高达100.0%,F1-Score为94.6%,Accuracy为89.9%,以及mAP达到了97.1%,均表现出了明显的优势。结论测试结果充分凸显了基于深度学习的自动诊断体系在腰椎MRI图像中椎间盘识别与分割方面的巨大潜力,同时在临床应用中可提高疾病诊断的准确性和效率,进而减轻医疗专业人员的负担。
Objective Lumbar disc herniation(LDH)is a common type of degenerative spinal disease that can lead to low back pain and neurological symptoms in the lower limbs.MRI images are crucial in diagnosis but suffer from empirical dependence and lack of standardisation mechanisms.The aim of this study was to develop a deep learning model based on CBAM-YOLO to assist in the automated diagnosis of lumbar disc herniation MRI images in order to improve the accuracy and efficiency of diagnosis.Methods A publicly available dataset containing MRI image data of 643 LDH patients was adopted in this study,and each image was labelled in detail with a variety of structural information including intervertebral discs and others.This study proposes a novel model,CBAM-YOLO,which is improved from the original YOLO-v8 model by embedding the convolutional attention module CBAM.This improvement helps the model to identify the feature location and spatial distribution information of the herniated disc more accurately.In order to fully validate the performance of the model,this study used the CBAM-YOLO model to systematically train the dataenhanced training set for a total of 100 training cycles.In the model evaluation session,this study used several evaluation metrics such as Precision,Recall,F1-Score,Accuracy,and mAP in order to comprehensively and rigorously assess the performance of the model.Results The CBAM-YOLO model demonstrated excellent performance in diagnosing lumbar disc herniation MRI images.Compared with the original YOLO-v8 model,the CBAM-YOLO model constructed in this study showed significant advantages in Precision of 89.9%,Recall of up to 100.0%,F1-Score of 94.6%,Accuracy of 89.9%,and mAP of 97.1%.Conclusion The test results fully highlight the great potential of deep learning-based automated diagnostic systems for disc identification and segmentation in lumbar spine MRI images,as well as the potential to improve the accuracy and efficiency of disease diagnosis in clinical applications,thereby reducing the burden on healthc
作者
李亚浩
沈学强
姜宏
俞鹏飞
LI Yahao;SHEN Xueqiang;JIANG Hong;YU Pengfei(Suzhou Hospital of Tranditional Chinese Medicine of Nanjing University of Chinese Meidcine,Suzhou 215009,Jiangsu,China)
出处
《中西医结合慢性病杂志》
2024年第1期63-69,共7页
JOURNAL OF INTEGRATED TRADITIONAL CHINESE AND WESTERN MEDICINE ON CHRONIC DISEASES
基金
苏州市姑苏卫生人才项目(GSWS2021049)
苏州市科技计划项目(SKY2023066)
苏州市“科教兴卫”青年科技项目(KJXW2023047)。
关键词
深度学习
腰椎间盘突出症
目标检测
卷积注意力机制模块
deep learning
lumbar disc herniation
target detection
convolutional block attention module
