Deployable real-time spinal endoscopic instance segmentation with lightweight multi-scale attention mechanism

Real-time instance segmentation in spinal endoscopy is vital for identifying and protecting key anatomy, but is hampered by narrow views, specular highlights, smoke/bleeding artifacts, fuzzy boundaries, and large-scale variation. Deployment further demands accuracy, speed, and stability under small-batch (often batch-size-one) settings. We present EndoSeg-RT, a deployable real-time framework co-designed across backbone, neck, and head with lightweight multi-scale attention. The backbone, C2f-Pro, combines RepViT-style re-parameterized convolutions with efficient multi-scale attention: multi-branch training for rich features that collapses to a single low-latency path at inference. The neck strengthens cross-scale consistency and boundary quality via Scale-Sequence Feature Fusion and Triple Feature Encoding, enhancing high-resolution anatomy. The head uses a Lightweight Multi-task Shared Head with shared convolutions and GroupNorm to cut redundancy and stabilize batch-size-one training/inference. We also release a clinically reviewed PELD dataset of 610 images from 61 patients with instance-level labels for adipose tissue, bone, ligamentum flavum, and nerve. Extensive experiments show EndoSeg-RT delivers competitive or superior accuracy with markedly lower complexity-only 1.8M parameters and 8.8 GFLOPs-outperforming heavier instance segmentation baselines. The model generalizes well to a public dental instance segmentation benchmark, demonstrating robustness beyond spinal endoscopy. Code and dataset are publicly available at: https://github.com/hhwmortal/PELD-Instance-segmentation.