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Updated: Jun 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

使用生成混合增强和自我训练进行交叉模式瘤细分.

Guillaume Salle, Gustavo Andrade-Miranda, Pierre-Henri Conze

IEEE transactions on bio-medical engineering

|April 1, 2024

概括

本研究引入了生成混合增强 (GBA),通过增强训练数据多样性来改善交叉模式图像细分. 该方法在前置性阴道瘤细分方面取得了最佳表现,解决了数据稀缺性和域转移挑战.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
机器学习机器学习

背景情况:

数据稀缺和领域的转变造成了偏见的训练集,无法代表现实世界的部署条件.
交叉模式图像细分旨在使用不同成像模式的标记数据对未标记的图像进行细分.

研究的目的:

开发一种新的交叉模式细分方法,解决数据的局限性.
改进对不同成像环境中的细分模型的概括性.

主要方法:

提出了一种使用图像合成增强生成混合增强 (GBA) 的交叉模式细分方法.
在GBA中利用SinGAN模型从单个图像中学习生成特征,使瘤外观多样化并减轻合成错误.
集成的GBA与使用伪标签的代自我训练程序,以进一步提高细分模型的概括性.

主要成果:

拟议的方法在MICCAI CrossMoDA 2022挑战赛中在前庭瘤 (VS) 分类中获得了第一名.
在验证和测试套件上表现出卓越的性能,具有最佳的平均子相似性和平均对称表面距离测量.

结论:

生成混合增强 (GBA) 通过提高数据多样性和补偿图像合成限制,有效地提高了细分模型的性能.

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局部对比度改变和代自我训练的组合显示了改善跨各种医学成像应用程序的细分的前景.