Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations

Segmentation and quantification of each subtype of emphysema is helpful to monitor chronic obstructive pulmonary disease. Due to the nature of emphysema (diffuse pulmonary disease), it is very difficult for experts to allocate semantic labels to every pixel in the CT images. In practice, partially annotating is a better choice for the radiologists to reduce their workloads. In this paper, we propose a new end-to-end trainable semi-supervised framework for semantic segmentation of emphysema with partial annotations, in which a segmentation network is trained from both annotated and unannotated areas. In addition, we present a new loss function, referred to as Fisher loss, to enhance the discriminative power of the model and successfully integrate it into our proposed framework. Our experimental results show that the proposed methods have superior performance over the baseline supervised approach (trained with only annotated areas) and outperform the state-of-the-art methods for emphysema segmentation.