# SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation

arXiv: https://arxiv.org/abs/2604.17451 · HTML: https://mlsanity.com/paper/2604.17451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 19 Apr 2026.

## Builds on (most cited first, 26 of 26)

- [Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation](https://mlsanity.com/paper/1906.02611/) (2019)
- [MedSAM2: Segment Anything in 3D Medical Images and Videos](https://mlsanity.com/paper/2504.03600/) (2025)
- [BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset](https://mlsanity.com/paper/2308.11298/) (2023)
- [Efficient Learning With Sine-Activated Low-rank Matrices](https://mlsanity.com/paper/2403.19243/) (2024)
- [SegReg: Segmenting OARs by Registering MR Images and CT Annotations](https://mlsanity.com/paper/2311.06956/) (2023)
- [MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction](https://mlsanity.com/paper/2502.00631/) (2025)
- [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)
- [JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA](https://mlsanity.com/paper/2404.11525/) (2024)
- [MediAug: Exploring Visual Augmentation in Medical Imaging](https://mlsanity.com/paper/2504.18983/) (2025)
- [MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection](https://mlsanity.com/paper/2409.00204/) (2024)
- [SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies](https://mlsanity.com/paper/2412.19990/) (2024)
- [GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation](https://mlsanity.com/paper/2501.12844/) (2025)
- [PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images](https://mlsanity.com/paper/2503.17970/) (2025)
- [Test-time generative augmentation for medical image segmentation](https://mlsanity.com/paper/2406.17608/) (2024)
- [ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer](https://mlsanity.com/paper/2501.01392/) (2025)
- [PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection](https://mlsanity.com/paper/2502.14063/) (2025)
- [Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey](https://mlsanity.com/paper/2410.14769/) (2024)
- [MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training](https://mlsanity.com/paper/2407.19546/) (2024)
- [DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps](https://mlsanity.com/paper/2502.15885/) (2025)
- [Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching](https://mlsanity.com/paper/2503.04826/) (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)
- [MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation](https://mlsanity.com/paper/2508.11032/) (2025)
- [Unified Medical Image Segmentation with State Space Modeling Snake](https://mlsanity.com/paper/2507.12760/) (2025)

## Abstract (arXiv)

Increasingly advanced data augmentation techniques have greatly aided clinical medical research, increasing data diversity and improving model generalization capabilities. Although most current basic models exhibit strong generalization abilities, image quality varies due to differences in equipment and operators. To address these challenges, we present SegTTA, a framework that improves medical image segmentation without model retraining by combining four augmentations (Gamma correction, Contrast enhancement, Gaussian blur, Gaussian noise) with weighted voting across multiple MedSAM2 checkpoints. Experiments demonstrate consistent improvements across three diverse datasets: healthy uterus segmentation, uterine myoma detection, and multi class hepatic structure segmentation. Ablation studies reveal that large organs benefit from intensity augmentations while small lesions require noise augmentations. The voting threshold controls the coverage precision trade off, enabling task specific optimization for different clinical requirements. Ultimately, on a multiclass hepatic vessel dataset, compared to MedSAM2 baselines, our method achieves an increase of 1.6 in mIoU and 1.9 in aIoU, along with a reduction of approximately 2.0 in HD95. Code will be available at https://github.com/AIGeeksGroup/SegTTA.