Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection

• URL: http://arxiv.org/abs/2408.05892v4
• Date: Sat, 7 Sep 2024 23:28:35 GMT
• Title: Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection
• Authors: Mobina Mansoori, Sajjad Shahabodini, Jamshid Abouei, Konstantinos N. Plataniotis, Arash Mohammadi,
• Abstract summary: Polyp segmentation plays a crucial role in the early detection and diagnosis of colorectal cancer. Recently, Meta AI Research released a general Segment Anything Model 2 (SAM 2), which has demonstrated promising performance in several segmentation tasks. In this manuscript, we evaluate the performance of SAM 2 in segmenting polyps under various prompted settings.
• Score: 18.61909523131399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Polyp segmentation plays a crucial role in the early detection and diagnosis of colorectal cancer. However, obtaining accurate segmentations often requires labor-intensive annotations and specialized models. Recently, Meta AI Research released a general Segment Anything Model 2 (SAM 2), which has demonstrated promising performance in several segmentation tasks. In this manuscript, we evaluate the performance of SAM 2 in segmenting polyps under various prompted settings. We hope this report will provide insights to advance the field of polyp segmentation and promote more interesting work in the future. This project is publicly available at https://github.com/ sajjad-sh33/Polyp-SAM-2.

Related papers

• Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model [18.61909523131399]
  This paper presents a novel approach to polyp segmentation by integrating the Segment Anything Model (SAM 2) with the YOLOv8 model. Our method leverages YOLOv8's bounding box predictions to autonomously generate input prompts for SAM 2, thereby reducing the need for manual annotations. We conducted exhaustive tests on five benchmark colonoscopy image datasets and two colonoscopy video datasets, demonstrating that our method exceeds state-of-the-art models in both image and video segmentation tasks.
  arXiv  Detail & Related papers  (2024-09-14T17:11:37Z)
• SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation [51.90445260276897]
  We prove that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models. We propose a simple but effective framework, termed SAM2-UNet, for versatile image segmentation.
  arXiv  Detail & Related papers  (2024-08-16T17:55:38Z)
• 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)
• Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation? [0.0]
  Polyp-SAM++, a text prompt-aided SAM, can better utilize a SAM using text prompting for robust and more precise polyp segmentation. We will evaluate the performance of a text-guided SAM on the polyp segmentation task on benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-12T17:45:39Z)
• Polyp-SAM: Transfer SAM for Polyp Segmentation [2.4492242722754107]
  Segment Anything Model (SAM) has recently gained much attention in both natural and medical image segmentation. We propose Poly-SAM, a finetuned SAM model for polyp segmentation, and compare its performance to several state-of-the-art polyp segmentation models. Our Polyp-SAM achieves state-of-the-art performance on two datasets and impressive performance on three datasets, with dice scores all above 88%.
  arXiv  Detail & Related papers  (2023-04-29T16:11:06Z)
• Can SAM Segment Polyps? [43.259797663208865]
  Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings.
  arXiv  Detail & Related papers  (2023-04-15T15:41:10Z)
• Lesion-aware Dynamic Kernel for Polyp Segmentation [49.63274623103663]
  We propose a lesion-aware dynamic network (LDNet) for polyp segmentation. It is a traditional u-shape encoder-decoder structure incorporated with a dynamic kernel generation and updating scheme. This simple but effective scheme endows our model with powerful segmentation performance and generalization capability.
  arXiv  Detail & Related papers  (2023-01-12T09:53:57Z)
• Automatic Polyp Segmentation via Multi-scale Subtraction Network [100.94922587360871]
  In clinical practice, precise polyp segmentation provides important information in the early detection of colorectal cancer. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. We propose a multi-scale subtraction network (MSNet) to segment polyp from colonoscopy image.
  arXiv  Detail & Related papers  (2021-08-11T07:54:07Z)
• PraNet: Parallel Reverse Attention Network for Polyp Segmentation [155.93344756264824]
  We propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images. We first aggregate the features in high-level layers using a parallel partial decoder (PPD) In addition, we mine the boundary cues using a reverse attention (RA) module, which is able to establish the relationship between areas and boundary cues.
  arXiv  Detail & Related papers  (2020-06-13T08:13:43Z)

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.