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通过整体损失函数优化提高CT图像分割精度

Chengyin Li^1,2, Rafi Ibn Sultan¹, Hassan Bagher-Ebadian^2,3,4,5

¹Department of Computer Science, Wayne State University, Detroit, Michigan, USA.

Medical physics

|April 25, 2025

概括

在CT医疗图像细分中优化整体损失函数显著提高了准确性. 有权重组合的可学习组合可以提高对单个或线性组合损失函数的细分性能.

科学领域:

医学图像分析医学图像分析
深度学习 (Deep Learning) 是一种深度学习.
计算成像技术的成像

背景情况:

深度神经网络训练CT医疗图像分割对损失功能选择敏感.
个别损失函数 (交叉,子,边界,TopK) 有限制和偏差.

研究的目的:

通过优化集合损失函数来提高细分精度.
为了减轻单损失函数及其线性组合固有的偏差和局限性.

主要方法:

通过线性组合和模型级合集评估集成损失函数 (交叉,子,边界,TopK).
在机构和公共CT数据集上使用了Attention U-Net (AttUNet) 和SwinUNETR架构.
采用静态平均和可学习的动态权重用于整体策略.

主要成果:

与非集体方法相比,集体方法使子相似系数 (DSC) 的得分提高了2%-7%.
在豪斯多夫距离 (HD) 和平均表面距离 (ASD) 中实现了显著的减少,例如,在直肠细分方面减少了19.1%的HD.
具有优化权重的可学习合奏产生了更细致的细分细节,并超过了静态合奏.

结论:

带有优化权重的组合模型有效地提高了医疗图像细分的准确性.
这种方法显示了推进自动化医疗图像分析应用的潜力.

关键词:

CT图像细分的部分化CT图像细分组合学习组合学习损失功能的优化优化损失功能的优化.

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