  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%.
