Deep learning for medical imaging is limited by data scarcity and domain shift, which lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal segmentation where the objective is to segment unlabelled domains using previously labelled images from other modalites, which is the context of the MICCAI CrossMoDA 2022 challenge on vestibular schwannoma (VS) segmentation. In this context, we propose a VS segmentation method that leverages conventional image-to-image translation and segmentation using iterative self training combined to a dedicated data augmentation technique called Generative Blending Augmentation (GBA). GBA is based on a one-shot 2D SinGAN generative model that allows to realistically diversify target tumor appearances in a downstream segmentation model, improving its generalization power at test time. Our solution ranked first on the VS segmentation task during the validation and test phase of the CrossModa 2022 challenge.

本文介绍了一种用于肿瘤分割的新型深度学习方法，该方法使用了基于图像转换和Segementation的迭代自训练算法以及一种名为GBA（Generative Blending Augmentation）的数据增强技术，使用SinGAN生成模型来使目标肿瘤出现得更加真实，从而提高了分割模型的泛化能力，并在MICCAI CrossMoDA 2022演示中的肿瘤分割任务中名列前茅。

使用生成混合增强和自训练的交叉模态肿瘤分割