LUCIDA: Low-dose Universal-tissue CT Image Domain Adaptation For Medical Segmentation

Accurate segmentation in low-dose CT scans remains a challenge in medical imaging, primarily due to the high annotation costs. This study introduces LUCIDA, a Low-dose Universal-tissue CT Image Domain Adaptation model operating under an unsupervised protocol without requiring LDCT annotations. It uniquely incorporates the Weighted Segmentation Reconstruction (WSR) module to establish a linear relationship between prediction maps and reconstructed images. By enhancing the quality of reconstructed images, LUCIDA improves the accuracy of prediction maps, facilitating a new domain adaptation framework. Extensive evaluation experiments demonstrate LUCIDA’s effectiveness in accurately recognizing a wide range of tissues, significantly outperforming traditional methods. We also introduce the LUCIDA-Ensemble model, demonstrating comparable performance to supervised learning models in organ segmentation and recognizing 112 tissue types.