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减少MR中的注释负担:一种新的MR对比导向对比学习方法用于图像分割

Lavanya Umapathy^1,2,3, Taylor Brown^2,4, Raza Mushtaq^2,4

¹Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona, USA.

Medical physics

|November 13, 2023

概括

约束式对比学习 (CCL) 通过嵌入组织特定信息来增强医疗图像细分的深度学习. 这种方法提高了具有有限标记数据的任务的性能,优于传统方法.

科学领域:

医疗成像医学成像
机器学习机器学习
计算机视觉计算机视觉

背景情况:

深度学习 (DL) 模型从预训练的对比学习中受益,特别是在医学图像细分中,注释有限.
学习域特定的局部表示对于改善DL模型在这种情况下的性能至关重要.

研究的目的:

扩展对比学习用于使用未标记数据进行磁共振 (MR) 图像细分.
利用特定域的对比信息来增强下游MR图像细分任务,使用有限的标记数据.

主要方法:

提出了一种新的受约束对比学习 (CCL) 策略,使用通过约束地图的组织特定信息.
定义了对比学习的积极和消极局部社区,将组织属性嵌入到表示空间中.
在多器官细分 (T2加权图像) 和瘤细分 (多参数BraTS数据集) 中已证明有用.

主要成果:

在所有细分任务中,CCL的策略始终改善了子得分,精度和回忆.
观察到的性能与监督基线可比,注释工作减少.
t-SNE可视化证实了T2信息的嵌入;多对比预训练进一步改进了BraTS细分.

结论:

通过CCL嵌入特定组织信息可以提高DL模型在MR图像细分中的性能.
CCL提供了一种有前途的方法来提高性能,并解决医疗图像细分中的数据稀缺问题.

关键词:

在MR中,对比度很高.MR细分的细分化 MR的细分化相反的学习学习学习.深度学习是一种深度学习.

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