A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation

• URL: http://arxiv.org/abs/2509.05809v1
• Date: Sat, 06 Sep 2025 19:02:53 GMT
• Title: A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation
• Authors: Tyler Ward, Abdullah Imran,
• Abstract summary: We introduce Probabilistic SAM, a probabilistic extension of the Segment Anything Model (SAM)<n>By incorporating a latent variable space and training with a variational objective, our model learns to generate diverse and plausible segmentation masks.<n>We evaluate Probabilistic SAM on the public LIDC-IDRI lung nodule dataset and demonstrate its ability to produce diverse outputs that align with expert disagreement.
• Score: 0.790660895390689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in promptable segmentation, such as the Segment Anything Model (SAM), have enabled flexible, high-quality mask generation across a wide range of visual domains. However, SAM and similar models remain fundamentally deterministic, producing a single segmentation per object per prompt, and fail to capture the inherent ambiguity present in many real-world tasks. This limitation is particularly troublesome in medical imaging, where multiple plausible segmentations may exist due to annotation uncertainty or inter-expert variability. In this paper, we introduce Probabilistic SAM, a probabilistic extension of SAM that models a distribution over segmentations conditioned on both the input image and prompt. By incorporating a latent variable space and training with a variational objective, our model learns to generate diverse and plausible segmentation masks reflecting the variability in human annotations. The architecture integrates a prior and posterior network into the SAM framework, allowing latent codes to modulate the prompt embeddings during inference. The latent space allows for efficient sampling during inference, enabling uncertainty-aware outputs with minimal overhead. We evaluate Probabilistic SAM on the public LIDC-IDRI lung nodule dataset and demonstrate its ability to produce diverse outputs that align with expert disagreement, outperforming existing probabilistic baselines on uncertainty-aware metrics. Our code is available at: https://github.com/tbwa233/Probabilistic-SAM/.

Related papers

• ProSAM: Enhancing the Robustness of SAM-based Visual Reference Segmentation with Probabilistic Prompts [15.582637232358177]
  We introduce ProSAM, a simple but effective method to address the stability challenges we identified in existing SAM-based visual reference segmentation approaches.<n>ProSAM avoids generating prompts that lie in unstable regions, overcoming the instability caused by less robust prompts.<n>Our approach consistently surpasses state-of-the-art methods on the Pascal-5$i$ and COCO-20$i$ datasets.
  arXiv  Detail & Related papers  (2025-06-27T00:50:15Z)
• SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement [40.37217744643069]
  We propose a universal and efficient approach by adapting SAM to the mask refinement task.<n>Specifically, we introduce a multi-prompt excavation strategy to mine diverse input prompts for SAM.<n>We extend our method to SAMRefiner++ by introducing an additional IoU adaption step to further boost the performance of the generic SAMRefiner on the target dataset.
  arXiv  Detail & Related papers  (2025-02-10T18:33:15Z)
• PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model [83.35198885088093]
  PolSAR data presents unique challenges due to its rich and complex characteristics.<n>Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used.<n>Most feature extraction networks for PolSAR are small, limiting their ability to capture features effectively.<n>We propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), an enhanced Segment Anything Model (SAM) that integrates domain-specific scattering characteristics and a novel prompt generation strategy.
  arXiv  Detail & Related papers  (2024-12-17T09:59:53Z)
• Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning [63.55145330447408]
  We propose a novel textbfSelf-textbfPerceptinon textbfTuning (textbfSPT) method for anomaly segmentation.<n>The SPT method incorporates a self-drafting tuning strategy, which generates an initial coarse draft of the anomaly mask, followed by a refinement process.
  arXiv  Detail & Related papers  (2024-11-26T08:33:25Z)
• Adapting Segment Anything Model for Unseen Object Instance Segmentation [70.60171342436092]
  Unseen Object Instance (UOIS) is crucial for autonomous robots operating in unstructured environments. We propose UOIS-SAM, a data-efficient solution for the UOIS task. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder.
  arXiv  Detail & Related papers  (2024-09-23T19:05:50Z)
• FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms [60.195642571004804]
  We introduce FlowSDF, an image-guided conditional flow matching framework, to represent an implicit distribution of segmentation masks.<n>Our framework enables accurate sampling of segmentation masks and the computation of relevant statistical measures.
  arXiv  Detail & Related papers  (2024-05-28T11:47:12Z)
• ASAM: Boosting Segment Anything Model with Adversarial Tuning [9.566046692165884]
  This paper introduces ASAM, a novel methodology that amplifies a foundation model's performance through adversarial tuning. We harness the potential of natural adversarial examples, inspired by their successful implementation in natural language processing. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations.
  arXiv  Detail & Related papers  (2024-05-01T00:13:05Z)
• Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery [15.748043194987075]
  This work assesses Segment Anything Model capabilities in segmenting objects of interest in the X-ray/infrared modalities. Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts. We find that infrared objects are also challenging to segment with point prompts given the low-contrast nature of this modality.
  arXiv  Detail & Related papers  (2024-04-18T16:04:14Z)
• Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation [20.557472889654758]
  The Segment Anything Model (SAM) gained significant success in natural image segmentation. Unlike natural images, many tissues and lesions in medical images have blurry boundaries and may be ambiguous. We propose a novel module called the Uncertainty-aware Adapter, which efficiently fine-tune SAM for uncertainty-aware medical image segmentation.
  arXiv  Detail & Related papers  (2024-03-16T14:11:54Z)
• 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)
• Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation [82.14087963690561]
  Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain.<n>We present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable.<n>The proposed approach showcases exceptional performance and efficacy on both simulated and real-world datasets.
  arXiv  Detail & Related papers  (2022-08-30T11:25:15Z)

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.