SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM

• URL: http://arxiv.org/abs/2304.05622v4
• Date: Sat, 3 Feb 2024 23:37:26 GMT
• Title: SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM
• Authors: Yihao Liu, Jiaming Zhang, Zhangcong She, Amir Kheradmand and Mehran Armand
• Abstract summary: We introduce Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer. SAMM achieves 0.6-second latency of a complete cycle and can infer image masks in nearly real-time.
• Score: 6.172995387355581
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Segment Anything Model (SAM) is a new image segmentation tool trained with the largest available segmentation dataset. The model has demonstrated that, with prompts, it can create high-quality masks for general images. However, the performance of the model on medical images requires further validation. To assist with the development, assessment, and application of SAM on medical images, we introduce Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer - an image processing and visualization software extensively used by the medical imaging community. This open-source extension to 3D Slicer and its demonstrations are posted on GitHub (https://github.com/bingogome/samm). SAMM achieves 0.6-second latency of a complete cycle and can infer image masks in nearly real-time.

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)
• 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)
• MAS-SAM: Segment Any Marine Animal with Aggregated Features [55.91291540810978]
  We propose a novel feature learning framework named MAS-SAM for marine animal segmentation. Our method enables to extract richer marine information from global contextual cues to fine-grained local details.
  arXiv  Detail & Related papers  (2024-04-24T07:38:14Z)
• 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)
• SAM3D: Segment Anything Model in Volumetric Medical Images [11.764867415789901]
  We introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike current SAM-based methods that segment volumetric data by converting the volume into separate 2D slices for individual analysis, our SAM3D model processes the entire 3D volume image in a unified approach.
  arXiv  Detail & Related papers  (2023-09-07T06:05:28Z)
• TomoSAM: a 3D Slicer extension using SAM for tomography segmentation [62.997667081978825]
  TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques.
  arXiv  Detail & Related papers  (2023-06-14T16:13:27Z)
• 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)
• Customized Segment Anything Model for Medical Image Segmentation [10.933449793055313]
  We build upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing large-scale models for medical image segmentation. SAMed applies the low-rank-based (LoRA) finetuning strategy to the SAM image encoder and finetunes it together with the prompt encoder and the mask decoder on labeled medical image segmentation datasets. Our trained SAMed model achieves semantic segmentation on medical images, which is on par with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-26T19:05:34Z)
• 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)

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.