Segment anything model 2: an application to 2D and 3D medical images

• URL: http://arxiv.org/abs/2408.00756v3
• Date: Thu, 22 Aug 2024 16:38:20 GMT
• Title: Segment anything model 2: an application to 2D and 3D medical images
• Authors: Haoyu Dong, Hanxue Gu, Yaqian Chen, Jichen Yang, Yuwen Chen, Maciej A. Mazurowski,
• Abstract summary: Segment Anything Model (SAM) has gained significant attention because of its ability to segment various objects in images given a prompt. Recently developed SAM 2 has extended this ability to video inputs. This opens an opportunity to apply SAM to 3D images, one of the fundamental tasks in the medical imaging field.
• Score: 16.253160684182895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segment Anything Model (SAM) has gained significant attention because of its ability to segment various objects in images given a prompt. The recently developed SAM 2 has extended this ability to video inputs. This opens an opportunity to apply SAM to 3D images, one of the fundamental tasks in the medical imaging field. In this paper, we extensively evaluate SAM 2's ability to segment both 2D and 3D medical images by first collecting 21 medical imaging datasets, including surgical videos, common 3D modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) as well as 2D modalities such as X-ray and ultrasound. Two evaluation settings of SAM 2 are considered: (1) multi-frame 3D segmentation, where prompts are provided to one or multiple slice(s) selected from the volume, and (2) single-frame 2D segmentation, where prompts are provided to each slice. The former only applies to videos and 3D modalities, while the latter applies to all datasets. Our results show that SAM 2 exhibits similar performance as SAM under single-frame 2D segmentation, and has variable performance under multi-frame 3D segmentation depending on the choices of slices to annotate, the direction of the propagation, the predictions utilized during the propagation, etc. We believe our work enhances the understanding of SAM 2's behavior in the medical field and provides directions for future work in adapting SAM 2 to this domain. Our code is available at: https://github.com/mazurowski-lab/segment-anything2-medical-evaluation.

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)
• SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners [87.76470518069338]
  We introduce SAM2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for promptable 3D segmentation. Our framework supports various prompt types, including 3D points, boxes, and masks, and can generalize across diverse scenarios, such as 3D objects, indoor scenes, sparse outdoor environments, and raw LiDAR. To our best knowledge, we present the most faithful implementation of SAM in 3D, which may serve as a starting point for future research in promptable 3D segmentation.
  arXiv  Detail & Related papers  (2024-08-29T17:59:45Z)
• Segment Anything in Medical Images and Videos: Benchmark and Deployment [8.51742337818826]
  We first present a comprehensive benchmarking of the Segment Anything Model 2 (SAM2) across 11 medical image modalities and videos. Then, we develop a transfer learning pipeline and demonstrate SAM2 can be quickly adapted to medical domain by fine-tuning. We implement SAM2 as a 3D slicer plugin and Gradio API for efficient 3D image and video segmentation.
  arXiv  Detail & Related papers  (2024-08-06T17:58:18Z)
• Interactive 3D Medical Image Segmentation with SAM 2 [17.523874868612577]
  We explore the zero-shot capabilities of SAM 2, the next-generation Meta SAM model trained on videos, for 3D medical image segmentation. By treating sequential 2D slices of 3D images as video frames, SAM 2 can fully automatically propagate annotations from a single frame to the entire 3D volume.
  arXiv  Detail & Related papers  (2024-08-05T16:58:56Z)
• Medical SAM 2: Segment medical images as video via Segment Anything Model 2 [4.911843298581903]
  We introduce Medical SAM 2 (MedSAM-2), an advanced segmentation model that addresses both 2D and 3D medical image segmentation tasks. By adopting the philosophy of taking medical images as videos, MedSAM-2 not only applies to 3D medical images but also unlocks new One-prompt capability. Our findings show that MedSAM-2 not only surpasses existing models in performance but also exhibits superior generalization across a range of medical image segmentation tasks.
  arXiv  Detail & Related papers  (2024-08-01T18:49:45Z)
• SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images [35.83393121891959]
  We introduce SAM-Med3D for general-purpose segmentation on volumetric medical images. SAM-Med3D can accurately segment diverse anatomical structures and lesions across various modalities. Our approach demonstrates that substantial medical resources can be utilized to develop a general-purpose medical AI.
  arXiv  Detail & Related papers  (2023-10-23T17:57:36Z)
• 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)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• 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)
• Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging [70.52819168140113]
  We use a dataset of over 20,000 subjects from the UK Biobank with both whole body Dixon technique magnetic resonance (MR) scans and also dual-energy x-ray absorptiometry (DXA) scans. We introduce a multi-modal image-matching contrastive framework, that is able to learn to match different-modality scans of the same subject with high accuracy. Without any adaption, we show that the correspondences learnt during this contrastive training step can be used to perform automatic cross-modal scan registration.
  arXiv  Detail & Related papers  (2021-07-14T12:35:05Z)

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.