CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation

• URL: http://arxiv.org/abs/2408.00181v1
• Date: Wed, 31 Jul 2024 22:24:05 GMT
• Title: CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation
• Authors: Shreyank N Gowda, David A. Clifton,
• Abstract summary: 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.
• Score: 20.448864959103858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Segment Anything Model (SAM) has achieved remarkable successes in the realm of natural image segmentation, but its deployment in the medical imaging sphere has encountered challenges. Specifically, the model struggles with medical images that feature low contrast, faint boundaries, intricate morphologies, and small-sized objects. To address these challenges and enhance SAM's performance in the medical domain, we introduce a comprehensive modification. Firstly, we incorporate a frozen Convolutional Neural Network (CNN) branch as an image encoder, which synergizes with SAM's original Vision Transformer (ViT) encoder through a novel variational attention fusion module. This integration bolsters the model's capability to capture local spatial information, which is often paramount in medical imagery. Moreover, to further optimize SAM for medical imaging, we introduce feature and position adapters within the ViT branch, refining the encoder's representations. We see that compared to current prompting strategies to fine-tune SAM for ultrasound medical segmentation, the use of text descriptions that serve as text prompts for SAM helps significantly improve the performance. Leveraging ChatGPT's natural language understanding capabilities, we generate prompts that offer contextual information and guidance to SAM, enabling it to better understand the nuances of ultrasound medical images and improve its segmentation accuracy. Our method, in its entirety, represents a significant stride towards making universal image segmentation models more adaptable and efficient in the medical domain.

Related papers

• DB-SAM: Delving into High Quality Universal Medical Image Segmentation [100.63434169944853]
  We propose a dual-branch adapted SAM framework, named DB-SAM, to bridge the gap between natural and 2D/3D medical data. Our proposed DB-SAM achieves an absolute gain of 8.8%, compared to a recent medical SAM adapter in the literature.
  arXiv  Detail & Related papers  (2024-10-05T14:36:43Z)
• 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)
• Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images [8.495954318776139]
  Segment Anything Model (SAM) has recently achieved amazing results in the field of natural image segmentation. In this paper, we mainly focus on ultrasound image segmentation. We develop a novel Breast Ultrasound SAM Adapter, termed Breast Ultrasound Segment Anything Model (BUSSAM)
  arXiv  Detail & Related papers  (2024-04-23T08:43:32Z)
• Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation [10.946806607643689]
  The Segment Anything Model (SAM) is a powerful foundation model that introduced revolutionary advancements in natural image segmentation. In this study, we introduce a novel fine-tuning framework that leverages SAM's ability to bundle and process multiple prompts per image.
  arXiv  Detail & Related papers  (2023-10-03T19:05:00Z)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting [10.308637269138146]
  We propose SAMUS as a universal model tailored for ultrasound image segmentation. We further enable it to work in an end-to-end manner denoted as AutoSAMUS. AutoSAMUS is realized by introducing an auto prompt generator (APG) to replace the manual prompt encoder of SAMUS.
  arXiv  Detail & Related papers  (2023-09-13T09:15:20Z)
• AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder [101.28268762305916]
  In this work, we replace Segment Anything Model with an encoder that operates on the same input image. We obtain state-of-the-art results on multiple medical images and video benchmarks. For inspecting the knowledge within it, and providing a lightweight segmentation solution, we also learn to decode it into a mask by a shallow deconvolution network.
  arXiv  Detail & Related papers  (2023-06-10T07:27:00Z)
• 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)
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation [78.01570371790669]
  Medical image segmentation is an essential prerequisite for developing healthcare systems. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard. We propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation.
  arXiv  Detail & Related papers  (2021-02-08T16:10:50Z)

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.