EMA-SAM: Exponential Moving-average for SAM-based PTMC Segmentation

• URL: http://arxiv.org/abs/2510.18213v1
• Date: Tue, 21 Oct 2025 01:30:27 GMT
• Title: EMA-SAM: Exponential Moving-average for SAM-based PTMC Segmentation
• Authors: Maryam Dialameh, Hossein Rajabzadeh, Jung Suk Sim, Hyock Ju Kwon,
• Abstract summary: EMA-SAM is a lightweight extension of SAM-2 that incorporates a confidence-weighted exponential moving average pointer into the memory bank.<n>On our PTMC-RFA dataset (124 minutes, 13 patients), EMA-SAM improves emphmaxDice from 0.82 to 0.86 and emphmaxIoU from 0.72 to 0.76, while reducing false positives by 29%.
• Score: 1.7674345486888503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

Related papers

• VesSAM: Efficient Multi-Prompting for Segmenting Complex Vessel [68.24765319399286]
  We present VesSAM, a powerful and efficient framework tailored for 2D vessel segmentation.<n>VesSAM integrates (1) a convolutional adapter to enhance local texture features, (2) a multi-prompt encoder that fuses anatomical prompts, and (3) a lightweight mask decoder to reduce jagged artifacts.<n>VesSAM consistently outperforms state-of-the-art PEFT-based SAM variants by over 10% Dice and 13% IoU.
  arXiv  Detail & Related papers  (2025-11-02T15:47:05Z)
• Fine-tuning Segment Anything for Real-Time Tumor Tracking in Cine-MRI [1.2560645967579729]
  We address the challenge of real-time tumor tracking in cine-MRI sequences of the thoracic and abdominal regions under strong data scarcity constraints.<n>Two complementary strategies were explored: (i) unsupervised registration with the IMPACT similarity metric and (ii) foundation model-based segmentation leveraging SAM 2.1.<n>The final model was selected based on the highest Dice Similarity Coefficient achieved on the validation set after fine-tuning.
  arXiv  Detail & Related papers  (2025-10-29T21:57:12Z)
• SAM2LoRA: Composite Loss-Guided, Parameter-Efficient Finetuning of SAM2 for Retinal Fundus Segmentation [0.5191474416940847]
  We propose a parameter-efficient fine-tuning strategy that adapts the Segment Anything Model 2 (SAM2) for fundus image segmentation.<n>SAM2LoRA integrates a low-rank adapter into both the image encoder and mask decoder, requiring fewer than 5% of the original trainable parameters.<n>It achieves Dice scores of up to 0.86 and 0.93 for blood vessel and optic disc segmentation, respectively, and AUC values of up to 0.98 and 0.99.
  arXiv  Detail & Related papers  (2025-10-11T17:07:44Z)
• Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2 [10.279314732888079]
  Short-Long Memory SAM 2 (SLM-SAM 2) is a novel architecture that integrates distinct short-term and long-term memory banks to improve segmentation accuracy.<n>We evaluate SLM-SAM 2 on three public datasets covering organs, bones, and muscles across MRI and CT modalities.
  arXiv  Detail & Related papers  (2025-05-03T16:16:24Z)
• RP-SAM2: Refining Point Prompts for Stable Surgical Instrument Segmentation [21.258106247629296]
  We introduce RP-SAM2, which incorporates a novel shift block and a compound loss function to stabilize point prompts.<n>Our approach reduces annotator reliance on precise point positioning while maintaining robust segmentation capabilities.<n> Experiments on the Cataract1k dataset demonstrate that RP-SAM2 improves segmentation accuracy, with a 2% mDSC gain, a 21.36% reduction in mHD95, and decreased variance across random single-point prompt results compared to SAM2.
  arXiv  Detail & Related papers  (2025-03-25T18:59:23Z)
• SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms [60.35639972035727]
  The lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. Dice scores reached up to 0.838 $pm$ 0.066 and 0.716 $pm$ 0.125 on the respective datasets, with an average performance of up to 0.804 $pm$ 0.15.
  arXiv  Detail & Related papers  (2024-11-14T17:06:00Z)
• SAMPa: Sharpness-aware Minimization Parallelized [51.668052890249726]
  Sharpness-aware (SAM) has been shown to improve the generalization of neural networks. Each SAM update requires emphsequentially computing two gradients, effectively doubling the per-iteration cost. We propose a simple modification of SAM, termed SAMPa, which allows us to fully parallelize the two gradient computations.
  arXiv  Detail & Related papers  (2024-10-14T16:21:23Z)
• Is SAM 2 Better than SAM in Medical Image Segmentation? [0.6144680854063939]
  The Segment Anything Model (SAM) has demonstrated impressive performance in zero-shot promptable segmentation on natural images. The recently released Segment Anything Model 2 (SAM 2) claims to outperform SAM on images and extends the model's capabilities to video segmentation. We conducted extensive studies using multiple datasets to compare the performance of SAM and SAM 2.
  arXiv  Detail & Related papers  (2024-08-08T04:34:29Z)
• ASPS: Augmented Segment Anything Model for Polyp Segmentation [77.25557224490075]
  The Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation. SAM's Transformer-based structure prioritizes global and low-frequency information. CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-06-30T14:55:32Z)
• Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation [10.911758824265736]
  We propose cineCMR-SAM, which incorporates both temporal and spatial information through a modified model architecture. Our model achieved superior data-specific model segmentation accuracy on the STACOM2011 dataset. We introduce a text prompt feature in cineCMR-SAM to specify the view type of input slices.
  arXiv  Detail & Related papers  (2024-03-15T04:27:55Z)
• Stable Segment Anything Model [79.9005670886038]
  The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts. This paper presents the first comprehensive analysis on SAM's segmentation stability across a diverse spectrum of prompt qualities. Our solution, termed Stable-SAM, offers several advantages: 1) improved SAM's segmentation stability across a wide range of prompt qualities, while 2) retaining SAM's powerful promptable segmentation efficiency and generality.
  arXiv  Detail & Related papers  (2023-11-27T12:51:42Z)
• Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer [158.2634766682187]
  Deep neural networks often suffer from poor generalization due to complex and non- unstructured loss landscapes. SharpnessAware Minimization (SAM) is a popular solution that smooths the loss by minimizing the change of landscape when adding a perturbation. In this paper, we propose Sparse SAM (SSAM), an efficient and effective training scheme that achieves perturbation by a binary mask.
  arXiv  Detail & Related papers  (2023-06-30T09:33:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.