Automatic medical imaging segmentation via self-supervising large-scale convolutional neural networks.

ABSTRACT:

Purpose

This study aims to develop a robust, large-scale deep learning model for medical image segmentation, leveraging self-supervised learning to overcome the limitations of supervised learning and data variability in clinical settings.

Methods and materials

We curated a substantial multi-center CT dataset for self-supervised pre-training using masked image modeling with sparse submanifold convolution. We designed a series of Sparse Submanifold U-Nets (SS-UNets) of varying sizes and performed self-supervised pre-training. We fine-tuned the SS-UNets on the TotalSegmentator dataset. The evaluation encompassed robustness tests on four unseen datasets and transferability assessments on three additional datasets.

Results

Our SS-UNets exhibited superior performance in com

SUBMITTER: Li Y 

PROVIDER: S-EPMC11938206 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature