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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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标签设置对基于深度学习的前列腺细分在MRI上的影响

Jakob Meglič1,2, Mohammed R S Sunoqrot3,4, Tone Frost Bathen3,4

  • 1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, 7030, Trondheim, Norway. jakobmeg@stud.ntnu.no.

Insights into imaging
|September 25, 2023
PubMed
概括

手动细分标签选择显著影响深度学习前列腺细分性能. 自动细分模型显示了比手工方法更高的一致性,并展示了真正的学习能力.

关键词:
深度学习是一种深度学习.标签 标签 标签 标签这就是为什么MRI是MRI.前列腺 前列腺前列腺分段化 分段化 分段化 分段化

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 前列腺癌的诊断方法 前列腺癌的诊断方法

背景情况:

  • 前列腺细分对于计算机辅助的前列腺癌检测和诊断至关重要.
  • 深度学习 (DL) 方法在前列腺和区域细分方面表现出色.
  • 手动细分 (标签) 选择对DL模型性能的影响仍未得到充分探索.

研究的目的:

  • 调查手动标签集选择对基于DL的前列腺细分性能的影响.
  • 评估使用不同的专家标签集如何影响细分准确性和一致性.
  • 将DL模型的性能与手动细分协议进行比较.

主要方法:

  • 从PROSTATEx I挑战数据集中使用了两个不同的专家标签集 (n=198).
  • 整合了额外的内部数据集 (n=233) 用于全面评估.
  • 使用nnU-Net框架进行前列腺自动细分.

主要成果:

  • 标签组的选择显著影响了模型性能 (p < 0.001).
  • 用相同的标签组进行训练和测试的模型表现出明显更高的性能 (p < 0.001).
  • 自动细分显示了明显更高的协议 (p < 0.0001) 比手动细分,与模型优于人类标签者.

结论:

  • 手动细分标签组的选择可测量地影响了基于DL的前列腺细分性能.
  • 与手动细分相比,基于DL的细分显示出更高的读者间协议.
  • 对于强大的DL模型,需要进一步考虑标签集选择,多中心细分和程序协议.