Cross-modal tumor segmentation using generative blending augmentation and self training

TL;DR

This paper introduces a novel cross-modal tumor segmentation method that combines generative blending augmentation with self-training, significantly improving segmentation accuracy in challenging medical imaging scenarios.

Contribution

The paper presents a new data augmentation technique called Generative Blending Augmentation (GBA) using SinGAN, combined with self-training, to enhance cross-modal tumor segmentation performance.

Findings

Ranked first in MICCAI CrossMoDA 2022 challenge for vestibular schwannoma segmentation.

Achieved best mean Dice similarity and surface distance measures.

Demonstrated performance improvements through local contrast alteration and iterative self-training.

Abstract

\textit{Objectives}: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities. \textit{Methods}: We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique called Generative Blending Augmentation (GBA). GBA leverages a SinGAN model to learn representative generative features from a single training image to diversify realistically tumor appearances. This way, we compensate for image synthesis errors, subsequently improving the generalization power of a downstream segmentation model. The proposed augmentation is further combined to an iterative self-training procedure leveraging pseudo labels at each pass. \textit{Results}: The proposed solution ranked first for vestibular schwannoma (VS) segmentation during the validation and test phases of the MICCAI CrossMoDA 2022 challenge, with best mean Dice similarity and average symmetric surface distance measures. \textit{Conclusion and significance}: Local contrast alteration of tumor appearances and iterative self-training with pseudo labels are likely to lead to performance improvements in a variety of segmentation contexts.