学习可迁移的以对象为中心的微分同胚变换用于医学图像分割中的数据增强
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation
AI总结：
针对医学图像分割数据标注困难，提出以对象为中心的微分同胚变换增强模型，学习形状变化并原位增强，跨数据集迁移有效提升肾脏肿瘤分割。
摘要：
在医学图像分割中获取标注数据具有挑战性，因为需要专家进行像素级标注。近期研究表明，对感兴趣对象应用可变形变换进行增强有助于缓解这一难题。然而，这些变换是在图像全局层面学习的，限制了其跨数据集的迁移性或在不便于图像对齐的问题中的适用性。尽管以对象为中心的增强为克服这些问题提供了很好的机会，但现有工作仅关注位置和随机变换，未考虑对象的形状变化。为此，我们提出了一种新颖的以对象为中心的数据增强模型，该模型能够学习感兴趣对象的形状变化，并在不修改图像其余部分的情况下对对象进行原位增强。我们通过利用从同一数据集内部以及从外部数据集迁移学习到的形状变化，证明了该方法在改善肾脏肿瘤分割方面的有效性。
英文摘要：
Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of interest with deformable transformations can help mitigate this challenge. However, these transformations have been learned globally for the image, limiting their transferability across datasets or applicability in problems where image alignment is difficult. While object-centric augmentations provide a great opportunity to overcome these issues, existing works are only focused on position and random transformations without considering shape variations of the objects. To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image. We demonstrated its effectiveness in improving kidney tumour segmentation when leveraging shape variations learned both from within the same dataset and transferred from external datasets.
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