Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation
This work addresses the challenge of reducing annotation requirements for medical image segmentation, which is incremental as it applies existing SSL methods to a specific dataset.
The paper tackles the problem of segmenting abdominal organs and tumors in CT scans with limited annotated data by using semi-supervised learning and iterative pseudo labeling, achieving an average DSC score of 89.63% for organs and 46.07% for tumors on a validation leaderboard.
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.