AnatoMix: Anatomy-aware Data Augmentation for Multi-organ Segmentation
Chang Liu Fuxin Fan Annette Schwarz Andreas Maier
Multi-organ segmentation in medical images is a widely researched task and
can save much manual efforts of clinicians in daily routines. Automating the
organ segmentation process using deep learning (DL) is a promising solution and
state-of-the-art segmentation models are achieving promising accuracy. In this
work, We proposed a novel data augmentation strategy for increasing the
generalizibility of multi-organ segmentation datasets, namely AnatoMix. By
object-level matching and manipulation, our method is able to generate new
images with correct anatomy, i.e. organ segmentation mask, exponentially
increasing the size of the segmentation dataset. Initial experiments have been
done to investigate the segmentation performance influenced by our method on a
public CT dataset. Our augmentation method can lead to mean dice of 76.1,
compared with 74.8 of the baseline method.