Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT

Wentao Liu, Tian Tong, weijin xu, Wang LeMeng, Haoyuan li, Huihua Yang

Keywords: Abdominal organ segmentation, Pan-cancer segmentation, Self-training, Mean teacher

Abstract: Abdominal organ and tumour segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manual assessment is inherently subjective with considerable inter- and intra-expert variability. In the paper, we propose a hybrid supervised framework, StMt, that integrates self-training and mean teacher for the segmentation of abdominal organs and tumors using partially labeled and unlabeled data. We introduce a two-stage segmentation pipeline and whole-volume-based input strategy to maximize segmentation accuracy while meeting the requirements of inference time and GPU memory usage. Experiments on the testing set of FLARE2023 demonstrate that our method achieves excellent segmentation performance as well as fast and low-resource model inference. Our method achieved an average DSC score of 90.48% and 50.00% for the organs and lesions on the testing set and the average running time and area under GPU memory-time cure are 11.1 seconds and 8979 MB, respectively.

Submission Number: 24