A Weakly-Supervised Multi-lesion Segmentation Framework Based on Target-Level Incomplete Annotations

Jianguo Ju, Shumin Ren, Dandan Qiu, Huijuan Tu, Juanjuan Yin, Pengfei Xu, Ziyu Guan

Abstract: Effectively segmenting Crohn’s disease (CD) from computed tomography is crucial for clinical use. Given the difficulty of obtaining manual annotations, more and more researchers have begun to pay attention to weakly supervised methods. However, due to the challenges of designing weakly supervised frameworks with limited and complex medical data, most existing frameworks tend to study single-lesion diseases ignoring multi-lesion scenarios. In this paper, we propose a new local-to-global weakly supervised neural framework for effective CD segmentation. Specifically, we develop a novel weak annotation strategy called Target-level Incomplete Annotation (TIA). This strategy only annotates one region on each slice as a labeled sample, which significantly relieves the burden of annotation. We observe that the classification networks can discover target regions with more details when replacing the input images with their local views. Taking this into account, we first design a TIA-based affinity cropping network to crop multiple local views with global anatomical information from the global view. Then, we leverage a local classification branch to extract more detailed features from multiple local views. Our framework utilizes a local views-based class distance loss and cross-entropy loss to optimize local and global classification branches to generate high-quality pseudo-labels that can be directly used as supervisory information for the semantic segmentation network. Experimental results show that our framework achieves an average DSC score of 47.8% on the CD71 dataset. Our code is available at https://github.com/HeyJGJu/CD_cTIA.