Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation

• URL: http://arxiv.org/abs/2404.06362v2
• Date: Tue, 30 Apr 2024 15:58:32 GMT
• Title: Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
• Authors: Sidra Aleem, Fangyijie Wang, Mayug Maniparambil, Eric Arazo, Julia Dietlmeier, Guenole Silvestre, Kathleen Curran, Noel E. O'Connor, Suzanne Little,
• Abstract summary: We propose a simple unified framework, SaLIP, for organ segmentation. SAM is used for part based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest. Finally, SAM is prompted by the retrieved ROI to segment a specific organ.
• Score: 10.444726122035133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training and fine tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un prompted SAM. Code and text prompts are available at: https://github.com/aleemsidra/SaLIP.

Related papers

• SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation [88.80792308991867]
  Segment Anything model (SAM) has shown ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. This paper presents SAM-CP, a simple approach that establishes two types of composable prompts beyond SAM and composes them for versatile segmentation. Experiments show that SAM-CP achieves semantic, instance, and panoptic segmentation in both open and closed domains.
  arXiv  Detail & Related papers  (2024-07-23T17:47:25Z)
• 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)
• PosSAM: Panoptic Open-vocabulary Segment Anything [58.72494640363136]
  PosSAM is an open-vocabulary panoptic segmentation model that unifies the strengths of the Segment Anything Model (SAM) with the vision-native CLIP model in an end-to-end framework. We introduce a Mask-Aware Selective Ensembling (MASE) algorithm that adaptively enhances the quality of generated masks and boosts the performance of open-vocabulary classification during inference for each image.
  arXiv  Detail & Related papers  (2024-03-14T17:55:03Z)
• Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively [69.97238935096094]
  The Open-Vocabulary SAM is a SAM-inspired model designed for simultaneous interactive segmentation and recognition. Our method can segment and recognize approximately 22,000 classes.
  arXiv  Detail & Related papers  (2024-01-05T18:59:22Z)
• 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)
• Guided Prompting in SAM for Weakly Supervised Cell Segmentation in Histopathological Images [27.14641973632063]
  This paper focuses on using weak supervision -- annotation from related tasks -- to induce a segmenter. Recent foundation models, such as Segment Anything (SAM), can use prompts to leverage additional supervision during inference. All SAM-based solutions hugely outperform existing weakly supervised image segmentation models, obtaining 9-15 pt Dice gains.
  arXiv  Detail & Related papers  (2023-11-29T11:18:48Z)
• 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)
• SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation [65.52097667738884]
  We introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to integrate surgical-specific information with SAM's pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning.
  arXiv  Detail & Related papers  (2023-08-17T02:51:01Z)
• 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)
• Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model [36.015065439244495]
  The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. This paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models.
  arXiv  Detail & Related papers  (2023-04-22T07:11:53Z)
• 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)

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.