The semantic segmentation of pelvic organs via MRI has important clinical significance. Recently, deep learning-enabled semantic segmentation has facilitated the three-dimensional geometric reconstruction of pelvic floor organs, providing clinicians with accurate and intuitive diagnostic results. However, the task of labeling pelvic floor MRI segmentation, typically performed by clinicians, is labor-intensive and costly, leading to a scarcity of labels. Insufficient segmentation labels limit the precise segmentation and reconstruction of pelvic floor organs. To address these issues, we propose a semi-supervised framework for pelvic organ segmentation. The implementation of this framework comprises two stages. In the first stage, it performs self-supervised pre-training using image restoration tasks. Subsequently, fine-tuning of the self-supervised model is performed, using labeled data to train the segmentation model. In the second stage, the self-supervised segmentation model is used to generate pseudo labels for unlabeled data. Ultimately, both labeled and unlabeled data are utilized in semi-supervised training. Upon evaluation, our method significantly enhances the performance in the semantic segmentation and geometric reconstruction of pelvic organs, Dice coefficient can increase by 2.65% averagely. Especially for organs that are difficult to segment, such as the uterus, the accuracy of semantic segmentation can be improved by up to 3.70%.

翻译：盆底器官的MRI语义分割具有重要的临床意义。近年来，基于深度学习的语义分割技术促进了盆底器官的三维几何重建，为临床医生提供了准确直观的诊断结果。然而，通常由临床医生完成的盆底MRI分割标注任务劳动强度大且成本高昂，导致标注数据稀缺。标注样本不足限制了盆底器官的精确分割与重建。针对这些问题，我们提出了一种用于盆底器官分割的半监督框架。该框架的实施包含两个阶段：第一阶段通过图像复原任务进行自监督预训练，随后利用标注数据对自监督模型进行微调以训练分割模型；第二阶段使用自监督分割模型为未标注数据生成伪标签。最终，标注数据与未标注数据共同参与半监督训练。经评估，我们的方法显著提升了盆底器官语义分割与几何重建的性能，Dice系数平均可提升2.65%。尤其对于子宫等难以分割的器官，语义分割精度最高可提升3.70%。