Wu Chih-Ying, Yeh Wei-Chang, Chang Shiaw-Meng, Hsu Che-Wei, Lin Zi-Jie
Department of Neurosurgery, China Medical University Hsinchu Hospital, Hsinchu 302, Taiwan.
Engineering Management, National Tsing Hua University, Hsinchu 300044, Taiwan.
Bioengineering (Basel). 2024 Sep 29;11(10):981. doi: 10.3390/bioengineering11100981.
Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.
近年来，人工智能作为计算机技术中一个快速发展的领域，已获得了广泛关注。随着计算机硬件的不断进步，深度学习在人工智能领域取得了突破性进展。在过去几年中，将深度学习架构应用于医学和工业异常检测，为解决众多与效率和准确性相关的挑战做出了重大贡献。为了在放射、病理、内镜、超声和生化检查中取得优异结果，本文利用深度学习结合图像处理来识别椎管和椎间孔尺寸。在现有研究中，磁共振成像（MRI）处理中的腐蚀和膨胀等技术也提高了结果的准确性。还提供了面积和交并比（IoU）等指标用于评估。其中，通过YOLOv4识别椎间孔（IVF）和椎间盘（IVD）的平均精度均值（mAP）为95.6%。采用Resnet50混合U-Net来识别椎管和椎间孔，IoU得分分别为79.1%和80.89%。
