Learning multi-organ and tumor segmentation from partially labeled datasets by a conditional dynamic attention network

Multi-organ segmentation is a critical prerequisite for many clinical applications. Deep learning-based approaches have recently achieved promising results on this task. However, they heavily rely on massive data with multi-organ annotated, which is labor- and expert-intensive and thus difficult to obtain. In contrast, single-organ datasets are easier to acquire, and many well-annotated ones are publicly available. It leads to the partially labeled issue: How to learn a unified multi-organ segmentation model from several single-organ datasets? Pseudo-label-based methods and conditional information-based methods make up the majority of existing solutions, where the former largely depends on the accuracy of pseudo-labels, and the latter has a limited capacity for task-related features. In this paper, we propose the Conditional Dynamic Attention Network (CDANet). Our approach is designed with two key components: (1) multisource parameter generator, fusing the conditional and multiscale information to better distinguish among different tasks, and (2) dynamic attention module, promoting more attention to task-related features. We have conducted extensive experiments on seven partially labeled challenging datasets. The results show that our method achieved competitive results compared with the advanced approaches, with an average Dice score of 75.08%. Additionally, the Hausdorff Distance is 26.31, which is a competitive result.