Lung Opacity Segmentation in Chest CT Images Using Multi-Head and Multi-Channel U-Nets with Partially Supervised Learning

There has been a large amount of research applying deep learning to the medical field. However, obtaining sufficient training data is challenging in the medical domain because annotation requires specialized knowledge and significant effort. This is especially true for segmentation tasks, where preparing fully annotated data for every pixel within an image is difficult. To address this, we propose methods to extract useful features for segmentation using two types of U-net-based networks and partially supervised learning with incomplete annotated data. This research specifically focuses on the segmentation of diffuse lung disease opacities in chest CT images. In our dataset, each image is partially annotated with a single type of lung opacity. To tackle this, we designed two distinct U-net architectures: a multi-head U-net, which utilizes a shared encoder and separated decoders for each opacity type, and a multi-channel U-net, which shares the encoder and decoder layers for more efficient feature learning. Furthermore, we integrated partially supervised learning with these networks. This involves employing distinct loss functions to both bring annotated regions (ground truth) and segmented regions (predictions) closer, and to push them apart, thereby suppressing erroneous predictions. In our experiments, we trained the models on partially annotated data and subsequently tested them on fully annotated data to compare the segmentation performance of each method. The results show that the multi-channel model applying partially supervised learning achieved the best performance while also reducing the number of weight parameters.