Partial consistent adversarial unified framework for unsupervised non-contrast CT cross-domain adaptation and segmentation

If kidney tumors can be segmented using non-contrast computed tomography (NC-CT) imaging alone, it would represent a major clinical advancement. Yet, to our best knowledge, existing kidney tumor segmentation methods rely heavily on high-resolution, high-sensitivity Contrast-Enhanced Computed Tomography (CE-CT) imaging due to the challenges of complex anatomy and morphology and weak boundaries of tumors. Moreover, these methods typically treat domain adaptation and segmentation as Separate Steps, with a primary focus on global domain adaptation, lacking the ability to prioritize tumor. To address above challenges, we propose a novel Unified Cross-domain Adaptation and Segmentation (UCAS) framework for unsupervised NC-CT kidney tumor adaptation and segmentation. Specifically, (1) UCAS integrates domain adaptation and segmentation into a novel unified framework, making the domain adaptation tumor-sensitive and addressing the limitations in focusing on tumor semantics during domain adaptation. (2) To bridge domain adaptation and segmentation, a novel Partial Consistent learning is proposed that leverages the partial semantics of tumors from synthetic CE-CT imaging to directly guide the domain adaptation. Furthermore, to address low-contrast and low-sensitivity challenges of tumor in NC-CT imaging, (3) a novel Partial Adversarial Learning is proposed to maintain the consistency between CE-CT and synthetic CE-CT imaging within local tumor semantic space. Moreover, to eliminate the semantic gap between the adaptation and segmentation tasks, (4) an effective Semantic Contrastive Learning aligns the domain adaptation and segmentation. The experimental results evaluated from segmentation and domain adaptation affirm that UCAS can segment kidney tumors from NC-CT imaging, outperforming state-of-the-art cross-domain segmentation methods and demonstrating significant clinical value.