Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation

• URL: http://arxiv.org/abs/2310.10493v1
• Date: Mon, 16 Oct 2023 15:17:06 GMT
• Title: Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation
• Authors: SeungKyu Kim, Hyun-Jic Oh, Seonghui Min and Won-Ki Jeong
• Abstract summary: The Segment Anything Model (SAM) is a foundational model for image segmentation. We evaluate SAM's performance in zero-shot and fine-tuned scenarios on histopathology data. We propose a modification of SAM's decoder to make it useful for interactive histology image segmentation.
• Score: 3.677055050765245
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopathology data, specifically in region segmentation, has received relatively limited attention. In this paper, we evaluate SAM's performance in zero-shot and fine-tuned scenarios on histopathology data, with a focus on interactive segmentation. Additionally, we compare SAM with other state-of-the-art interactive models to assess its practical potential and evaluate its generalization capability with domain adaptability. In the experimental results, SAM exhibits a weakness in segmentation performance compared to other models while demonstrating relative strengths in terms of inference time and generalization capability. To improve SAM's limited local refinement ability and to enhance prompt stability while preserving its core strengths, we propose a modification of SAM's decoder. The experimental results suggest that the proposed modification is effective to make SAM useful for interactive histology image segmentation. The code is available at \url{https://github.com/hvcl/SAM_Interactive_Histopathology}

Related papers

• 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)
• 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)
• 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)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars [19.725817146049707]
  The Segment Anything Model (SAM) has demonstrated remarkable capabilities of scaled-up segmentation models. However, the adoption of foundational models in the medical domain presents a challenge due to the difficulty and expense of labeling sufficient data. This paper introduces an efficient and practical approach for fine-tuning SAM using a limited number of exemplars.
  arXiv  Detail & Related papers  (2023-08-27T15:21:25Z)
• SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT [3.2495192768429924]
  The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation. We conduct a comprehensive evaluation of SAM and its adaptations on a large-scale public dataset of OCTs from RETOUCH challenge. We showcase adapted SAM's efficacy as a powerful segmentation model in retinal OCT scans, although still lagging behind established methods in some circumstances.
  arXiv  Detail & Related papers  (2023-08-18T06:26:22Z)
• 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)
• Segment Anything Model for Medical Image Analysis: an Experimental Study [19.95972201734614]
  Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. We evaluate SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies.
  arXiv  Detail & Related papers  (2023-04-20T17:50:18Z)
• 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)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• 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.