KG-Seg: Medical Knowledge-Guided Target-State Learning for 3D Lesion Segmentation with Lesion-Only Annotations

In 3D segmentation with lesion-only annotations, lesion supervision defines voxel-wise foreground membership, whereas medical knowledge characterizes the relations between lesion states and relevant anatomical structures. How to integrate these two forms of information within a learnable target-state formulation is a key problem in medical knowledge-guided lesion segmentation. To address this problem, we propose KG-Seg, a medical knowledge-guided target-state learning framework. KG-Seg compiles symbolic medical relations, anatomical anchors, and empirical target-distribution priors into spatial–relational representations and calibrates their valid ranges and reliability strengths through knowledge–target consistency. On this basis, target-state learning is formulated as the joint structure of a knowledge-conditioned target learning distribution and a reliability-aware medical knowledge direction field. The former reconstructs the probability mass and relative contribution of different target states, aligning the learning distribution with lesion states supported by medical knowledge. The latter guides the optimization direction, contribution strength, and supervision validity associated with each target state through support, risk, and neutral directions. In this way, medical knowledge is transformed from an external prior into an intrinsic variable that organizes target-state learning. On MSD Lung and MSD Colon, KG-Seg increases Dice by 4.2 and 6.3 percentage points, respectively, relative to the nnUNetv2 baseline. These results indicate that the joint reconstruction of the target-state distribution and optimization direction establishes a learnable connection between the representation of medical relations and model optimization, providing a new paradigm for medical knowledge-guided learning in 3D lesion segmentation with lesion-only annotations.