Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation
Within medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous andadaptive segmentation (CHASe), which only requires a small labeled cohort of single phase data to adapt to any unlabeled cohort of heterogenous multi-phase data with possibly new clinical scenarios and pathologies. To do this, we develop a versatile framework that fuses appearance-based semi-supervision, mask-based adversarial domain adaptation, and pseudo-labeling. We also introduce co-heterogeneous training, which is a novel integration of co-training and hetero-modality learning. We evaluate CHASe using a clinically comprehensive and challenging dataset of multi-phase computed tomography (CT) imaging studies (1147 patients and 4577 3D volumes), with a test set of 100 patients. Compared to previous state-of-the-art baselines, CHASe can further improve pathological liver mask Dice-Sørensen coefficients by ranges of 4.2% to 9.4%, depending on the phase combinations, e.g., from 84.6% to 94.0% on non-contrast CTs."