Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation

TL;DR

This paper presents an iterative semi-supervised learning approach using pseudo-labeling to improve abdominal organ and tumor segmentation in CT scans, achieving high accuracy on the FLARE23 dataset.

Contribution

It introduces an iterative pseudo-labeling strategy with deep models to enhance segmentation performance using partially annotated data.

Findings

Achieved 89.63% DSC for organs and 46.07% for tumors on FLARE23.

Demonstrated effective semi-supervised learning with pseudo-labeling.

Improved segmentation accuracy over baseline methods.

Abstract

Deep-learning (DL) based methods are playing an important role in the task of abdominal organs and tumors segmentation in CT scans. However, the large requirements of annotated datasets heavily limit its development. The FLARE23 challenge provides a large-scale dataset with both partially and fully annotated data, which also focuses on both segmentation accuracy and computational efficiency. In this study, we propose to use the strategy of Semi-Supervised Learning (SSL) and iterative pseudo labeling to address FLARE23. Initially, a deep model (nn-UNet) trained on datasets with complete organ annotations (about 220 scans) generates pseudo labels for the whole dataset. These pseudo labels are then employed to train a more powerful segmentation model. Employing the FLARE23 dataset, our approach achieves an average DSC score of 89.63% for organs and 46.07% for tumors on online validation leaderboard. For organ segmentation, We obtain 0.9007\% DSC and 0.9493\% NSD. For tumor segmentation, we obtain 0.3785% DSC and 0.2842% NSD. Our code is available at https://github.com/USTguy/Flare23.