Semi-Supervised Segmentation of Computed Tomography Images Using Co-Training of Models With Heterogeneous Tasks

Collecting sufficient training data for medical image segmentation models has long been a research challenge. To address this, various studies have employed semi-supervised learning (SSL), which combines labeled and unlabeled data to enhance model training. In abdominal CT images, segmentation errors often occur due to similar Hounsfield Unit values between target organs and surrounding tissues. This issue is particularly pronounced in environments with limited data. This poses a significant challenge in SSL research. Our study proposes an SSL method that leverages adjacent CT images and labeled data to incorporate 3D spatial information. An interpolation model generates pseudo-labels from the labels of adjacent CT data, which are used in co-training with a segmentation model to iteratively enhance performance. To evaluate performance, we conducted experiments on diverse abdominal CT datasets. The results demonstrate that the two models can improve each other’s outputs, achieving competitive performance even with limited training data.