GUMP-Net: An interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation

2026-06-17

AI Summary

This study addresses the challenge of few-shot, multi-class pelvic segmentation for precision diagnosis and surgical navigation by proposing an interpretable, geometry-driven segmentation framework that uniquely integrates deep neural networks with an enhanced geodesic active contour model. The method comprises three synergistic modules—object detection, anatomy-aware edge detection, and level-set iterative evolution—to achieve highly accurate, robust, and anatomically consistent segmentation results. Evaluated on a pelvic dataset, the approach significantly outperforms existing methods and demonstrates successful generalization to complex anatomical structures such as the ankle joint, offering an effective tool for clinical applications including fracture reduction.

Abstract

Pelvic segmentation is one of the most important and fundamental research problems in precise and intelligent diagnosis and treatment, as well as surgical planning and navigation for pelvic fractures. By combining an improved geodesic active contour model with deep neural networks, we propose GUMP-Net, an interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation, in which three network modules are designed to constitute the overall segmentation framework together: the object detection module for automatic level set initialization, the edge detector module for learning an anatomy-aware edge detector function and the iteration module for deep level set evolution. Leveraging the advantages of level set representation and deep learning, GUMP-Net shows more accurate, robust and consistent segmentation performance, especially in small training data situation, compared to the state-of-the-art methods. Extensive experiments on pelvic datasets demonstrate the rationality and effectiveness of the proposed algorithm. Further experiments extended to ankle dataset indicate broader applications to other anatomies. The proposed algorithm not only provides an efficient segmentation method for complex fracture reduction, but also gives an interpretable geometric perspective for understanding deep learning segmentation.

Problem

• pelvic segmentation
• multi-class segmentation
• surgical planning
• intelligent diagnosis
• anatomical segmentation

Innovation

• interpretable segmentation
• level set evolution
• model-data-driven
• anatomy-aware edge detection
• multi-class pelvic segmentation