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.

通过学习感兴趣物体的形状变化，提出了一种新颖的物体中心数据增强模型，能够在不修改图像的其余部分的情况下，通过加强感兴趣物体来改善肾脏肿瘤分割的效果。