Unsupervised domain adaptation by cross-domain consistency learning for CT body composition

Computed tomography (CT) scans of the abdomen have become the gold standard for assessing body composition (BC). Accurate estimation of skeletal muscle and adipose tissues from CT scan slices is crucial for diagnosis and prognosis. Much research in abdominal image analysis focuses on the third lumbar vertebra (L3) due to its stability and ease of labeling compared to other lumbar vertebrae. This study leverages labeled L3 slices (source domain) to predict unlabeled slices from thoracic T1 to sacrum S5 region (target domain). We proposed a Twin Encoder–Decoder Network (TED-Net) with distinct weight initialization employing Cross-domain Consistency Learning (CDCL) for joint training across the domains. This strategy extends the network’s knowledge by enforcing consistency between predictions from two segmentation networks. The training objective includes supervised loss terms for the source domain and unsupervised loss terms for the target domain. This results in increases of 6.68%, 3.31%, and 4.40% in Precision, Dice Similarity Coefficient, and Intersection over Union, respectively, indicating significant improvement in performance on the target domain, suggesting that domain-invariant feature learning through cross-domain consistency learning enhances a network’s adaptability over unlabeled domains.