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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于CT的胰腺细分深度学习模型的比较

Abhinav Suri1, Pritam Mukherjee2, Perry J Pickhardt3

  • 1Radiology and Imaging Sciences, National Institutes of Health, Clinical Center, Bethesda, Maryland, USA; David Geffen School of Medicine at UCLA, Los Angeles, California, USA.

Academic radiology
|June 29, 2024
PubMed
概括
此摘要是机器生成的。

总分离器,腹膜天文图和AASwin模型表现出强大的胰腺细分性能. 然而,性能因扫描特征而异,表明需要超越总量指标的细微评估.

关键词:
人工智能的人工智能是人工智能.计算机断层扫描 (CT) 扫描胰腺是什么? 胰腺是什么?分段化 分段化 分段化 分段化

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 放射学和计算机病理学
  • 生物医学工程和机器学习

背景情况:

  • 在CT扫描上精确的胰腺细分对于诊断胰腺疾病和开发成像生物标志物至关重要.
  • 现有的研究往往依赖于综合性能指标,可能掩盖不同患者和扫描特征的模型性能变化.

研究的目的:

  • 用多个指标对五个领先的胰腺细分模型的性能进行基准测试.
  • 评估扫描特征如何影响细分性能,包括对比度状态和周围胰腺衰减.

主要方法:

  • 一项回顾性研究确定了五种高性能胰腺细分模型 (TotalSegmentator, Abdomen Atlas, nnUNetv1, AASwin, DM-UNet).
  • 模型在352个CT扫描中使用Dice分数,豪斯多夫距离和平均表面距离来评估模型.
  • 结果按对比度状态和周围胰腺衰减分层;多变量回归确定了与细分精度相关的因素.

主要成果:

  • 总分段, Abdomen Atlas 和 AASwin 是表现最好的公司, Dice 的得分在 77-80% 左右.
  • 在AASwin和nnUNetv1的非对比扫描中,性能下降 (P < .001).
  • 增加的周胰腺衰减对所有模型的Dice分数产生了负面影响,除了TotalSegmentator (P < .01).

结论:

  • 在各种数据集 (TotalSegmentator, Abdomen Atlas, AASwin) 上训练的基于卷积神经网络的模型表现最好.
  • 总分类器实现了与在较大的数据集上训练的模型相似的结果,突出了训练数据的效率.
  • 患者和扫描特征之间的差异性性能需要全面评估,超出临床适用性的总量指标.