bibtext: @InProceedings{ Che_LUCIDA_MICCAI2024,
                   author = { Chen, Yixin and Meng, Xiangxi and Wang, Yan and Zeng, Shuang and Liu, Xi and Xie, Zhaoheng },
                   title = { { LUCIDA: Low-dose Universal-tissue CT Image Domain Adaptation For Medical Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: LUCIDA: Low-dose Universal-tissue CT Image Domain Adaptation For Medical Segmentation
authors: Chen, Yixin Meng, Xiangxi Wang, Yan Zeng, Shuang Liu, Xi Xie, Zhaoheng