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 validation 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 89.79\% and 45.55 \% for the organs and lesions on the validation set and the average running time and area under GPU memory-time cure are 11.25s and 9627.82MB, respectively.

翻译：腹部器官与肿瘤分割在器官量化、手术规划及疾病诊断等多项重要临床应用中具有关键作用。然而，人工评估存在固有的主观性，且专家间及专家自身的评判标准差异显著。本文提出一种混合监督框架StMt，通过整合自训练与平均教师方法，利用部分标注与未标注数据实现腹部器官及肿瘤分割。我们引入两阶段分割流程与基于全容积的输入策略，在满足推理时间与GPU内存需求的同时最大化分割精度。在FLARE2023验证集上的实验表明，该方法在实现卓越分割性能的同时，兼具快速与低资源模型推理能力。本方法在验证集上对器官与病灶的平均DSC评分分别达89.79%与45.55%，平均运行时间及GPU内存-时间曲线下面积分别为11.25秒与9627.82MB。