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计算机工程 ›› 2024, Vol. 50 ›› Issue (3): 1-15. doi: 10.19678/j.issn.1000-3428.0067502

• 热点与综述 • 上一篇    下一篇

深度学习在脊柱图像分割中的应用综述

姜百浩, 刘静*(), 仇大伟, 姜良   

  1. 山东中医药大学智能与信息工程学院, 山东 济南 250355
  • 收稿日期:2023-04-25 出版日期:2024-03-15 发布日期:2024-03-13
  • 通讯作者: 刘静
  • 基金资助:
    国家自然科学基金面上项目(82174528); 国家自然科学基金面上项目(81973981); 山东中医药大学青年科研创新团队项目(校字[2020]54号); 山东省专业学位研究生教学案例库建设项目(SDYAL21054); 山东省本科教学改革研究项目(M2020207)

Review of Deep Learning Applications in Spinal Image Segmentation

Baihao JIANG, Jing LIU*(), Dawei QIU, Liang JIANG   

  1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong, China
  • Received:2023-04-25 Online:2024-03-15 Published:2024-03-13
  • Contact: Jing LIU

摘要:

深度学习算法在脊柱图像分割中具有学习和自适应能力强、对图像有非线性映射能力等优点,相较于传统分割方法,能更好地提取脊柱图像中的关键信息,并且抑制不相关信息,辅助医生准确定位病灶区域,实现精准、高效分割。从深度学习算法、脊柱疾病类型、图像类型、实验分割结果、性能评估指标等方面,对深度学习在脊柱图像分割中的应用现状进行归纳、总结并加以分析。介绍深度学习模型和脊柱图像分割的背景,从而引出深度学习在脊柱图像分割中的应用;介绍常见的几种脊柱疾病类型,阐述其在图像分割中的难点,并介绍脊柱图像分割中常用的公开数据集、图像分割的方法流程以及图像分割评价指标等要素;结合具体实验总结分析基于卷积神经网络模型、U型网络模型及其改进的模型在椎骨、椎间盘以及脊柱肿瘤图像分割中的应用进展;结合以往的实验结果和当前深度学习模型的研究进展,总结目前临床研究的局限性以及分割效果不足的原因,针对所存在的问题提出相应的解决方法,并对未来的研究和发展进行展望。

关键词: 深度学习, 卷积神经网络, U型网络, 脊柱疾病, 图像分割

Abstract:

Deep learning algorithms have the advantages of strong learning, strong adaptive, and unique nonlinear mapping abilities in spinal image segmentation. Compared with traditional segmentation methods, they can better extract key information from spinal images and suppress irrelevant information, which can assist doctors in accurately locating focal areas and realizing accurate and efficient segmentation. The application status of deep learning in spinal image segmentation is summarized and analyzed as concerns deep learning algorithms, types of spinal diseases, types of images, experimental segmentation results, and performance evaluation indicators. First, the background of the deep learning model and spinal image segmentation is described, and thereafter, the application of deep learning in spinal image segmentation is introduced. Second, several common types of spinal diseases are introduced, the difficulties in image segmentation are described, and common open datasets, image segmentation method flow, and image segmentation evaluation indicators are introduced in spinal image segmentation. Combined with specific experiments, the application progress of the Convolutional Neural Network(CNN) model, the U-Net model, and their improved models in the image segmentation of vertebrae, intervertebral discs, and spinal tumors are summarized and analyzed. Combined with previous experimental results and the current research progress of deep learning models, this paper summarizes the limitations of current clinical studies and the reasons for the insufficient segmentation effect, and proposes corresponding solutions to the existing problems. Finally, prospects for future studies and development are proposed.

Key words: deep learning, Convolutional Neural Network(CNN), U-Net, spinal diseases, image segmentation