EMA-SAM: Exponential Moving-average for SAM-based PTMC Segmentation
This work addresses the challenge of accurate and stable tumour tracking in interventional ultrasound for medical applications, representing an incremental improvement over existing methods.
The paper tackled the problem of unstable lesion segmentation in ultrasound videos for papillary thyroid microcarcinoma (PTMC) during radio-frequency ablation (RFA) by introducing EMA-SAM, a lightweight extension of SAM-2 that improves maxDice from 0.82 to 0.86 and maxIoU from 0.72 to 0.76 on a PTMC-RFA dataset.
Papillary thyroid microcarcinoma (PTMC) is increasingly managed with radio-frequency ablation (RFA), yet accurate lesion segmentation in ultrasound videos remains difficult due to low contrast, probe-induced motion, and heat-related artifacts. The recent Segment Anything Model 2 (SAM-2) generalizes well to static images, but its frame-independent design yields unstable predictions and temporal drift in interventional ultrasound. We introduce \textbf{EMA-SAM}, a lightweight extension of SAM-2 that incorporates a confidence-weighted exponential moving average pointer into the memory bank, providing a stable latent prototype of the tumour across frames. This design preserves temporal coherence through probe pressure and bubble occlusion while rapidly adapting once clear evidence reappears. On our curated PTMC-RFA dataset (124 minutes, 13 patients), EMA-SAM improves \emph{maxDice} from 0.82 (SAM-2) to 0.86 and \emph{maxIoU} from 0.72 to 0.76, while reducing false positives by 29\%. On external benchmarks, including VTUS and colonoscopy video polyp datasets, EMA-SAM achieves consistent gains of 2--5 Dice points over SAM-2. Importantly, the EMA pointer adds \textless0.1\% FLOPs, preserving real-time throughput of $\sim$30\,FPS on a single A100 GPU. These results establish EMA-SAM as a robust and efficient framework for stable tumour tracking, bridging the gap between foundation models and the stringent demands of interventional ultrasound. Codes are available here \hyperref[code {https://github.com/mdialameh/EMA-SAM}.