MFA-ICPS: Semi-supervised medical image segmentation with improved cross pseudo supervision and multi-dimensional...

In the medical field, medical image segmentation plays a pivotal role in facilitating disease evaluation and supporting treatment decision-making for doctors. Recently, deep learning methods have been employed in the field of medical image segmentation. However, the manual annotation of a large number of reliable labels is a costly and time-consuming process.

To address this challenge, a semi-supervised learning framework is required to alleviate the burden of reliable labeling and enhance segmentation accuracy in challenging areas of medical images.

Therefore, this paper presents MFA-ICPS framework, a semi-supervised learning framework based on the improved cross pseudo supervision (ICPS) and multi-dimensional feature attention (MFA) modules. Medical images inevitably contain some noise that may affect the segmentation accuracy, so the proposed framework addresses this challenge by introducing noise disturbance, combining ICPS and MFA modules, and using pseudo-segmentation maps and MFA maps to maintain the consistency at both the output and feature levels.

In the experiments, MFA-ICPS framework accurately obtains the following performances on the left atrial dataset: Dice, Jaccard, 95HD, and ASD values are $$ , $$ , 6.00 and 1.94 mm, respectively. And on the pancreas-CT dataset, the following performances are accurately obtained: Dice, Jaccard, 95HD, and ASD values are $$ , $$ , 7.67 and 1.65 mm, respectively.

The segmentation performance of MFA-ICPS framework on different medical datasets demonstrates its remarkable capability to significantly enhance medical image segmentation.