# Segment Any Medical Model Extended

arXiv: https://arxiv.org/abs/2403.18114 · HTML: https://mlsanity.com/paper/2403.18114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 28 of 28)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder](https://mlsanity.com/paper/2306.06370/) (2023)
- [SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model](https://mlsanity.com/paper/2304.05396/) (2023)
- [Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model](https://mlsanity.com/paper/2304.11332/) (2023)
- [DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures](https://mlsanity.com/paper/1803.08606/) (2018)
- [How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images](https://mlsanity.com/paper/2306.13731/) (2023)
- [Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation](https://mlsanity.com/paper/2304.12637/) (2023)
- [MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images](https://mlsanity.com/paper/2203.12362/) (2022)
- [AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation](https://mlsanity.com/paper/2308.03726/) (2023)
- [SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM](https://mlsanity.com/paper/2304.05622/) (2023)
- [Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration](https://mlsanity.com/paper/1911.07042/) (2019)
- [Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection](https://mlsanity.com/paper/1803.11078/) (2018)
- [Learnable Ophthalmology SAM](https://mlsanity.com/paper/2304.13425/) (2023)
- [mathrmSAMMed: A medical image annotation framework based on large vision model](https://mlsanity.com/paper/2307.05617/) (2023)
- [EVD Surgical Guidance with Retro-Reflective Tool Tracking and Spatial Reconstruction using Head-Mounted Augmented Reality Device](https://mlsanity.com/paper/2306.15490/) (2023)
- [Cross-modality Attention Adapter: A Glioma Segmentation Fine-tuning Method for SAM Using Multimodal Brain MR Images](https://mlsanity.com/paper/2307.01124/) (2023)
- [SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference](https://mlsanity.com/paper/2306.13988/) (2023)
- [Utilizing Segment Anything Model For Assessing Localization of GRAD-CAM in Medical Imaging](https://mlsanity.com/paper/2306.15692/) (2023)
- [Toward Process Controlled Medical Robotic System](https://mlsanity.com/paper/2308.05809/) (2023)

## Built on by (most cited first, 1 of 1)

- [A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys](https://mlsanity.com/paper/2409.04962/) (2024)

## Abstract (arXiv)

The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning, modifying, and evaluating SAM for analysis of medical imaging. An increasing number of works have been published focusing on the mentioned four directions, where variants of SAM are proposed. To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation. In this work, we introduce SAMM Extended (SAMME), a platform that integrates new SAM variant models, adopts faster communication protocols, accommodates new interactive modes, and allows for fine-tuning of subcomponents of the models. These features can expand the potential of foundation models like SAM, and the results can be translated to applications such as image-guided therapy, mixed reality interaction, robotic navigation, and data augmentation.