Pelvic floor MRI segmentation based on semi-supervised deep learning

• URL: http://arxiv.org/abs/2311.03105v2
• Date: Wed, 22 Nov 2023 15:46:00 GMT
• Title: Pelvic floor MRI segmentation based on semi-supervised deep learning
• Authors: Jianwei Zuo, Fei Feng, Zhuhui Wang, James A. Ashton-Miller, John O.L. Delancey and Jiajia Luo
• Abstract summary: Deep learning-enabled semantic segmentation has facilitated the three-dimensional geometric reconstruction of pelvic floor organs. The task of labeling pelvic floor MRI segmentation is labor-intensive and costly, leading to a scarcity of labels. Insufficient segmentation labels limit the precise segmentation and reconstruction of pelvic floor organs.
• Score: 3.764963091541598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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