Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation

• URL: http://arxiv.org/abs/2306.08958v1
• Date: Thu, 15 Jun 2023 08:51:24 GMT
• Title: Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation
• Authors: Chuyun Shen, Wenhao Li, Ya Zhang, Xiangfeng Wang
• Abstract summary: The Anything Model (SAM) has emerged as a foundation model for addressing image segmentation. This paper focuses on assessing the potential of SAM's zero-shot capabilities within the interactive medical image segmentation (IMIS) paradigm. We develop a framework that adaptively offers suitable prompt forms for human experts.
• Score: 22.242652304004068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high annotation cost, the medical image segmentation (MIS) community has been encouraged to investigate SAM's zero-shot capabilities to facilitate automatic annotation. Inspired by the extraordinary accomplishments of interactive medical image segmentation (IMIS) paradigm, this paper focuses on assessing the potential of SAM's zero-shot capabilities within the IMIS paradigm to amplify its benefits in the MIS domain. Regrettably, we observe that SAM's vulnerability to prompt forms (e.g., points, bounding boxes) becomes notably pronounced in IMIS. This leads us to develop a framework that adaptively offers suitable prompt forms for human experts. We refer to the framework above as temporally-extended prompts optimization (TEPO) and model it as a Markov decision process, solvable through reinforcement learning. Numerical experiments on the standardized benchmark BraTS2020 demonstrate that the learned TEPO agent can further enhance SAM's zero-shot capability in the MIS context.

Related papers

• Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation [56.87049651707208]
  Few-shot Semantic has evolved into In-context tasks, morphing into a crucial element in assessing generalist segmentation models. Our initial focus lies in understanding how to facilitate interaction between the query image and the support image, resulting in the proposal of a KV fusion method within the self-attention framework. Based on our analysis, we establish a simple and effective framework named DiffewS, maximally retaining the original Latent Diffusion Model's generative framework.
  arXiv  Detail & Related papers  (2024-10-03T10:33:49Z)
• CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation [20.448864959103858]
  The Segment Anything Model (SAM) has achieved remarkable successes in the realm of natural image segmentation. SAM struggles with medical images that feature low contrast, faint boundaries, intricate morphologies, and small-sized objects. We introduce a comprehensive modification to enhance SAM's performance in the medical domain.
  arXiv  Detail & Related papers  (2024-07-31T22:24:05Z)
• Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation [52.172885882728174]
  In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. We introduce a novel approach that leverages the advantages of online machine learning to enhance Segment Anything (SA) during test time. We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images.
  arXiv  Detail & Related papers  (2024-06-03T03:16:25Z)
• AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning [61.666973416903005]
  Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prompts. We propose a novel framework, termed AlignSAM, designed for automatic prompting for aligning SAM to an open context.
  arXiv  Detail & Related papers  (2024-06-01T16:21:39Z)
• Segment Any Medical Model Extended [39.80956010574076]
  We introduce SAMM Extended (SAMME), a platform that integrates new SAM variant models, adopts faster communication protocols, accommodates new interactive modes, and allows for fine-tuning of subcomponents of the models. These features can expand the potential of foundation models like SAM, and the results can be translated to applications such as image-guided therapy, mixed reality interaction, robotic navigation, and data augmentation.
  arXiv  Detail & Related papers  (2024-03-26T21:37:25Z)
• ProMISe: Promptable Medical Image Segmentation using SAM [11.710367186709432]
  We propose an Auto-Prompting Module (APM) which provides SAM-based foundation model with Euclidean adaptive prompts in the target domain. We also propose a novel non-invasive method called Incremental Pattern Shifting (IPS) to adapt SAM to specific medical domains. By coupling these two methods, we propose ProMISe, an end-to-end non-fine-tuned framework for Promptable Medical Image.
  arXiv  Detail & Related papers  (2024-03-07T02:48:42Z)
• Boosting Segment Anything Model Towards Open-Vocabulary Learning [69.42565443181017]
  Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model. Despite SAM finding applications and adaptations in various domains, its primary limitation lies in the inability to grasp object semantics. We present Sambor to seamlessly integrate SAM with the open-vocabulary object detector in an end-to-end framework.
  arXiv  Detail & Related papers  (2023-12-06T17:19:00Z)
• Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models [14.292149307183967]
  This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided masks to produce coarse pixel-wise prompts for SAM.
  arXiv  Detail & Related papers  (2023-11-22T07:07:55Z)
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [51.770805270588625]
  The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Recent studies and individual experiments have shown that SAM underperforms in medical image segmentation. We propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model.
  arXiv  Detail & Related papers  (2023-04-25T07:34:22Z)
• SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model [1.1221592576472588]
  We evaluate the zero-shot capabilities of the Segment Anything Model for medical image segmentation. We show that SAM generalizes well to CT data, making it a potential catalyst for the advancement of semi-automatic segmentation tools.
  arXiv  Detail & Related papers  (2023-04-10T18:20:29Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)

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.