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深度学习驱动的MRI三神经细分与SEVB-net.

Chuan Zhang1,2, Man Li3, Zheng Luo2

  • 1The First Affiliated Hospital, Jinan University, Guangzhou, China.

Frontiers in neuroscience
|November 3, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,SEVB-Net,在MRI扫描中自动细分三角神经,提高三角神经疼痛 (TN) 的诊断效率. 这种方法比现有的方法更快,更轻.

关键词:
自动细分自动细分自动细分深度学习是一种深度学习.磁共振成像技术的使用三角神经是三角神经的一部分.三分生神经疼痛 三分生神经疼痛

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经科学是一个神经科学.

背景情况:

  • 由于严重的疼痛,三神经疼痛 (TN) 的诊断和治疗具有挑战性.
  • 磁共振成像 (MRI) 对TN诊断和理解其病理学至关重要.
  • 在3DMRI中手动对三神经进行细分是耗时且主观的.

研究的目的:

  • 通过BottleNeck V-Net (SEVB-Net) 引入Squeeze和Excitation,这是一个新的深度学习模型.
  • 为了实现三神经在三维T2MRI体积中的自动细分.
  • 在时间和主观性方面克服手动细分的局限性.

主要方法:

  • 开发了SEVB-Net,用于使用3D T2 MRI图像进行端到端的培训.
  • 评估了与V-Net和nnUNet相比的SEVB-Net,使用Dice相似系数 (DSC),灵敏度和精度.
  • 使用Mann-Whitney U测试进行手动修改的手动和SEVB-Net之间的细分时间比较.

主要成果:

  • SEVB-Net实现了最先进的性能,当与 ωDoubleLoss.Loss相结合时,DSC从0.6070到0.7923不等.
  • 与nnUNet.net相比,SEVB-Net表现出更高的效率,显著减少参数 (2.20M),内存 (11.41MB) 和模型大小 (17.02MB).
  • 与手动方法相比,使用SEVB-Net的自动细分显著减少了细分时间 (p < 0.001).

结论:

  • SEVB-Net有效地在3D T2 MRI中自动化了三神经根和主要分支的细分.
  • SEVB-Net提供了与nnUNet相似的细分性能,但具有更轻量级的架构.
  • 拟议的SEVB-Net模型提高了MRI中三神经细分的计算效率和准确性.