SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation

• URL: http://arxiv.org/abs/2406.14819v1
• Date: Fri, 21 Jun 2024 01:42:20 GMT
• Title: SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation
• Authors: Quoc-Huy Trinh, Hai-Dang Nguyen, Bao-Tram Nguyen Ngoc, Debesh Jha, Ulas Bagci, Minh-Triet Tran,
• Abstract summary: We propose a framework that guides small segmentation models for polyp segmentation to address the cost challenge. In this study, we introduce the Edge Guiding module, which integrates edge information into image features. Our small models showcase their efficacy by achieving competitive results with state-of-the-art methods.
• Score: 6.709243857842895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Polyp segmentation, a critical concern in medical imaging, has prompted numerous proposed methods aimed at enhancing the quality of segmented masks. While current state-of-the-art techniques produce impressive results, the size and computational cost of these models pose challenges for practical industry applications. Recently, the Segment Anything Model (SAM) has been proposed as a robust foundation model, showing promise for adaptation to medical image segmentation. Inspired by this concept, we propose SAM-EG, a framework that guides small segmentation models for polyp segmentation to address the computation cost challenge. Additionally, in this study, we introduce the Edge Guiding module, which integrates edge information into image features to assist the segmentation model in addressing boundary issues from current segmentation model in this task. Through extensive experiments, our small models showcase their efficacy by achieving competitive results with state-of-the-art methods, offering a promising approach to developing compact models with high accuracy for polyp segmentation and in the broader field of medical imaging.

Related papers

• MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation [2.2585213273821716]
  We introduce MedCLIP-SAMv2, a novel framework that integrates the CLIP and SAM models to perform segmentation on clinical scans. Our approach includes fine-tuning the BiomedCLIP model with a new Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss. We also investigate using zero-shot segmentation labels within a weakly supervised paradigm to enhance segmentation quality further.
  arXiv  Detail & Related papers  (2024-09-28T23:10:37Z)
• ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation [49.42525661521625]
  This paper presents ShapeMamba-EM, a specialized fine-tuning method for 3D EM segmentation. It is tested over a wide range of EM images, covering five segmentation tasks and 10 datasets.
  arXiv  Detail & Related papers  (2024-08-26T08:59:22Z)
• Multi-scale Information Sharing and Selection Network with Boundary Attention for Polyp Segmentation [10.152504573356413]
  We propose a Multi-scale information sharing and selection network (MISNet) for polyp segmentation task. Experiments on five polyp segmentation datasets demonstrate that MISNet successfully improved the accuracy and clarity of segmentation result.
  arXiv  Detail & Related papers  (2024-05-18T02:48:39Z)
• MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation [2.2585213273821716]
  We propose a novel framework, called MedCLIP-SAM, that combines CLIP and SAM models to generate segmentation of clinical scans. By extensively testing three diverse segmentation tasks and medical image modalities, our proposed framework has demonstrated excellent accuracy.
  arXiv  Detail & Related papers  (2024-03-29T15:59:11Z)
• Explore In-Context Segmentation via Latent Diffusion Models [132.26274147026854]
  latent diffusion model (LDM) is an effective minimalist for in-context segmentation. We build a new and fair in-context segmentation benchmark that includes both image and video datasets.
  arXiv  Detail & Related papers  (2024-03-14T17:52:31Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation [6.148777307966648]
  We present KDAS, a Knowledge Distillation framework that incorporates attention supervision, and our proposed Symmetrical Guiding Module. This framework is designed to facilitate a compact student model with fewer parameters, allowing it to learn the strengths of the teacher model. Our compact models demonstrate their strength by achieving competitive results with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-13T23:00:48Z)
• Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation [45.15517909664628]
  Polyp segmentation plays a vital role in accurately locating polyps at an early stage. pixel-wise annotation for polyp images by physicians during the diagnosis is both time-consuming and expensive. We propose a novel SAM-guided Collaborative Learning Network (SAM-CLNet) for scribble-supervised polyp segmentation.
  arXiv  Detail & Related papers  (2023-12-01T03:07:13Z)
• Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation [14.135249795318591]
  We propose a novel perspective on self-prompting in medical vision applications. We harness the embedding space of the Segment Anything Model to prompt itself through a simple yet effective linear pixel-wise classifier. We achieve competitive results on multiple datasets.
  arXiv  Detail & Related papers  (2023-08-15T08:20:07Z)
• 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)
• Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung [91.3755431537592]
  We present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular U-Net architecture family but modified for the joint nodule segmentation and its texture classification tasks and an ensemble-based model for the follow-up recommendation.
  arXiv  Detail & Related papers  (2020-06-25T07:20:41Z)

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.