# When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

arXiv: https://arxiv.org/abs/2304.08506 · HTML: https://mlsanity.com/paper/2304.08506/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)

## Built on by (most cited first, 30 of 52)

- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [SAM-Med2D](https://mlsanity.com/paper/2308.16184/) (2023)
- [SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images](https://mlsanity.com/paper/2310.15161/) (2023)
- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2309.08842/) (2023)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024)
- [Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets](https://mlsanity.com/paper/2304.09324/) (2023)
- [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://mlsanity.com/paper/2306.06211/) (2023)
- [AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation](https://mlsanity.com/paper/2308.03726/) (2023)
- [Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2308.07624/) (2023)
- [SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation](https://mlsanity.com/paper/2308.07156/) (2023)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Data-Centric Foundation Models in Computational Healthcare: A Survey](https://mlsanity.com/paper/2401.02458/) (2024)
- [Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation](https://mlsanity.com/paper/2404.06362/) (2024)
- [SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective](https://mlsanity.com/paper/2304.14674/) (2023)
- [Segment Anything Model (SAM) for Radiation Oncology](https://mlsanity.com/paper/2306.11730/) (2023)
- [Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.03678/) (2023)
- [SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation](https://mlsanity.com/paper/2408.04593/) (2024)
- [FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection](https://mlsanity.com/paper/2312.14465/) (2023)
- [MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM](https://mlsanity.com/paper/2409.00924/) (2024)
- [Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping](https://mlsanity.com/paper/2305.07586/) (2023)
- [On Efficient Variants of Segment Anything Model: A Survey](https://mlsanity.com/paper/2410.04960/) (2024)
- [mathrmSAMMed: A medical image annotation framework based on large vision model](https://mlsanity.com/paper/2307.05617/) (2023)
- [M2OST: Many-to-one Regression for Predicting Spatial Transcriptomics from Digital Pathology Images](https://mlsanity.com/paper/2409.15092/) (2024)
- [Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation](https://mlsanity.com/paper/2502.00630/) (2025)
- [S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2408.06447/) (2024)
- [Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification](https://mlsanity.com/paper/2311.10529/) (2023)

## Abstract (arXiv)

Learning to segmentation without large-scale samples is an inherent capability of human. Recently, Segment Anything Model (SAM) performs the significant zero-shot image segmentation, attracting considerable attention from the computer vision community. Here, we investigate the capability of SAM for medical image analysis, especially for multi-phase liver tumor segmentation (MPLiTS), in terms of prompts, data resolution, phases. Experimental results demonstrate that there might be a large gap between SAM and expected performance. Fortunately, the qualitative results show that SAM is a powerful annotation tool for the community of interactive medical image segmentation.