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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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MAPSeg:统一无监督域调整用于基于3D掩盖自编码和伪标签的异质医疗图像细分.

Xuzhe Zhang1, Yuhao Wu2, Elsa Angelini1,3

  • 1Columbia University.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|October 31, 2024
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概括

MAPSeg是一种用于医疗图像细分的新框架,它使用无监督域调整 (UDA) 来克服数据限制. 这种统一的方法在各种医学成像数据集和UDA场景中实现了卓越的性能.

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科学领域:

  • 医学图像分析 医学图像分析
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 强大的医疗图像细分对于定量分析至关重要,但手动注释是昂贵和耗时的.
  • 无监督域调整 (UDA) 通过将知识从标记域转移到未标记域来解决标签稀缺问题.
  • 现有的UDA方法面临着异质和体积医疗数据以及多种不同领域转移类型的挑战.

研究的目的:

  • 引入掩盖自动编码和伪标签细分 (MAPSeg),一个多功能和高性能的UDA框架用于医疗图像细分.
  • 系统地解决医疗图像细分中的四个不同的领域转移挑战.
  • 制定适用于集中,联合和测试时间的UDA的框架.

主要方法:

  • 开发了MAPSeg,这是一个统一的框架,集成了面具自动编码和伪标签以进行细分.
  • 在四种不同的医学图像领域转移场景中系统评估MAPSeg.
  • 将MAPSeg与私人婴儿大脑MRI和公共心脏CT-MRI数据集的最新方法进行比较.

主要成果:

  • MAPSeg表现出卓越的性能,与现有方法相比,实现了显著的Dice系数改进 (10.5在MRI上,5.7在CT-MRI上).
  • 该框架在集中,联合和测试时间的UDA设置中保持了可比的性能.
  • MAPSeg有效地解决了异质和体积医学图像细分的挑战.

结论:

  • MAPSeg提供了一个多功能和有效的解决方案,用于医疗图像细分中的无监督域调整.
  • 该框架对现实应用程序具有显著的实用价值,这些应用程序需要使用有限的标签进行强大的细分.
  • 在医疗成像分析领域的领域转变方面,MAPSeg代表了重大进展.