# GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation

arXiv: https://arxiv.org/abs/2501.12844 · HTML: https://mlsanity.com/paper/2501.12844/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Builds on (most cited first, 24 of 24)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [PolarMask: Single Shot Instance Segmentation with Polar Representation](https://mlsanity.com/paper/1909.13226/) (2019)
- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Fast Interactive Object Annotation with Curve-GCN](https://mlsanity.com/paper/1903.06874/) (2019)
- [Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction](https://mlsanity.com/paper/2001.00208/) (2020)
- [BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset](https://mlsanity.com/paper/2308.11298/) (2023)
- [Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases](https://mlsanity.com/paper/2406.13674/) (2024)
- [SegReg: Segmenting OARs by Registering MR Images and CT Annotations](https://mlsanity.com/paper/2311.06956/) (2023)
- [ESA: Annotation-Efficient Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2408.13491/) (2024)
- [MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule](https://mlsanity.com/paper/2409.14028/) (2024)
- [SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation](https://mlsanity.com/paper/2408.00496/) (2024)
- [SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies](https://mlsanity.com/paper/2412.19990/) (2024)
- [Multi-Task, Multi-Domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets](https://mlsanity.com/paper/2105.10310/) (2021)

## Built on by (most cited first, 10 of 10)

- [MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction](https://mlsanity.com/paper/2502.00631/) (2025)
- [CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation](https://mlsanity.com/paper/2506.23121/) (2025)
- [PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images](https://mlsanity.com/paper/2503.17970/) (2025)
- [DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps](https://mlsanity.com/paper/2502.15885/) (2025)
- [SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation](https://mlsanity.com/paper/2506.08949/) (2025)
- [MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation](https://mlsanity.com/paper/2506.18679/) (2025)
- [Unified Medical Image Segmentation with State Space Modeling Snake](https://mlsanity.com/paper/2507.12760/) (2025)
- [A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation](https://mlsanity.com/paper/2502.05396/) (2025)
- [3D Medical Imaging Segmentation on Non-Contrast CT](https://mlsanity.com/paper/2503.08361/) (2025)
- [SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation](https://mlsanity.com/paper/2604.17451/) (2026)

## Abstract (arXiv)

Multi-organ segmentation is a critical yet challenging task due to complex anatomical backgrounds, blurred boundaries, and diverse morphologies. This study introduces the Gradient-aware Adaptive Momentum Evolution Deep Snake (GAMED-Snake) model, which establishes a novel paradigm for contour-based segmentation by integrating gradient-based learning with adaptive momentum evolution mechanisms. The GAMED-Snake model incorporates three major innovations: First, the Distance Energy Map Prior (DEMP) generates a pixel-level force field that effectively attracts contour points towards the true boundaries, even in scenarios with complex backgrounds and blurred edges. Second, the Differential Convolution Inception Module (DCIM) precisely extracts comprehensive energy gradients, significantly enhancing segmentation accuracy. Third, the Adaptive Momentum Evolution Mechanism (AMEM) employs cross-attention to establish dynamic features across different iterations of evolution, enabling precise boundary alignment for diverse morphologies. Experimental results on four challenging multi-organ segmentation datasets demonstrate that GAMED-Snake improves the mDice metric by approximately 2% compared to state-of-the-art methods. Code will be available at https://github.com/SYSUzrc/GAMED-Snake.