SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation
For medical imaging practitioners, SegTTA offers a training-free way to boost segmentation accuracy across diverse organs and lesions, though the gains are incremental.
SegTTA improves zero-shot medical image segmentation by combining four test-time augmentations with weighted voting across MedSAM2 checkpoints, achieving consistent gains on three datasets (e.g., +1.6 mIoU, +1.9 aIoU, -2.0 HD95 on hepatic vessels).
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.