DCN-KUnet: A DCNv3-Based Backbone and KAN Bottleneck for Chromosome Segmentation

Chromosome foreground segmentation is a binary semantic segmentation problem that serves as a prerequisite for overlap reasoning, contact-region inspection, and automated karyotyping. Although simpler than full instance separation in formulation, it remains difficult in metaphase imagery because chromosomes are elongated, deformable, weakly bounded, and frequently touching or partially overlapping. To address these chromosome-specific difficulties, we present DCN-KUnet as a task-oriented integration rather than a new generic segmentation family. The encoder–decoder backbone embeds DCNv3 modules to perform geometry-adaptive sampling for bending-aware and boundary-aware representation learning, while a B-spline KAN bottleneck refines the compressed semantic representation through lightweight nonlinear transformation. In addition, a hybrid objective composed of mask supervision, semantic consistency regularization, and internal feature regularization (Lcd+LSCR+LIFD) jointly constrains prediction accuracy, cross-stage semantic agreement, and feature compactness during training. Experiments on the public overlapping-chromosome dataset and on AutoKary2022 converted to binary foreground masks show that DCN-KUnet achieves stronger Dice, IoU, and HD95 with a moderate parameter budget. These results support the proposed framework as a practical and lightweight semantic foreground front-end for chromosome analysis pipelines rather than a full instance-disentanglement solution.