From 451507cb2e05837f3d88984da4fddd03d9fed034 Mon Sep 17 00:00:00 2001
From: JefferyJiang-YF <60686137+JefferyJiang-YF@users.noreply.github.com>
Date: Tue, 10 Sep 2024 18:38:32 +0800
Subject: [PATCH 1/2] Added a summary of existing work on SAM1 and SAM2 in
 medical field

---
 .DS_Store                                    | Bin 8196 -> 10244 bytes
 benchmark_backbones/.DS_Store                | Bin 0 -> 6148 bytes
 direct_inference/.DS_Store                   | Bin 0 -> 6148 bytes
 document/awesome_medical_segment_anything.md |  92 +++++++++++--------
 supervised_pretraining/.DS_Store             | Bin 0 -> 8196 bytes
 target_applications/.DS_Store                | Bin 8196 -> 10244 bytes
 6 files changed, 56 insertions(+), 36 deletions(-)
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 create mode 100644 direct_inference/.DS_Store
 create mode 100644 supervised_pretraining/.DS_Store

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diff --git a/document/awesome_medical_segment_anything.md b/document/awesome_medical_segment_anything.md
index d1ff211..87dafea 100644
--- a/document/awesome_medical_segment_anything.md
+++ b/document/awesome_medical_segment_anything.md
@@ -3,46 +3,66 @@
 [![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
 
 Dear Colleagues,
- 
+
 While we are not directly working on [Segment Anything Models (SAM)](https://segment-anything.com), our team at Johns Hopkins University has been collecting and annotating large-scale datasets for Medical SAM. We believe [our dataset](https://www.zongweiz.com/dataset) would be of great interest if you plan to adapt [SAM2](https://ai.meta.com/sam2/) to medical images.
- 
+
 We are progressively releasing **9,262 3D CT volumes** and **231,550 3D anatomical masks** to the public. In support of Medical SAM, we are happy to proactively share these data with you for beta test.
- 
+
 *Simply reply to this email <zzhou82@jh.edu> if you would like access to our dataset.*
 *We would greatly appreciate it if you could also forward this message to colleagues who might be interested in SAM for segmenting medical images.*
 
 Below is a list of papers relevant to Medical SAM(2). Feel free to create pull requests to make this list more comprehensive.
 
-| **title** | **paper** | **repo** |
-|-----------|:---------:|:---------:|
-| Interactive 3d medical image segmentation with sam 2 | [![arXiv](https://img.shields.io/badge/arXiv-2408.02635-b31b1b.svg)](https://arxiv.org/pdf/2408.02635) | [![GitHub stars](https://img.shields.io/github/stars/Chuyun-Shen/SAM_2_Medical_3D.svg?logo=github&label=Stars)](https://github.com/Chuyun-Shen/SAM_2_Medical_3D) |
-| SAM-UNet: Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images | [![arXiv](https://img.shields.io/badge/arXiv-2408.09886-b31b1b.svg)](https://arxiv.org/pdf/2408.09886) | [![GitHub stars](https://img.shields.io/github/stars/Hhankyangg/sam-unet.svg?logo=github&label=Stars)](https://github.com/Hhankyangg/sam-unet) |
-| Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging | [![arXiv](https://img.shields.io/badge/arXiv-2408.06170-b31b1b.svg)](https://arxiv.org/pdf/2408.06170) |  |
-| SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2408.08870-b31b1b.svg)](https://arxiv.org/pdf/2408.08870) | [![GitHub stars](https://img.shields.io/github/stars/WZH0120/SAM2-UNet.svg?logo=github&label=Stars)](https://github.com/WZH0120/SAM2-UNet) |
-| PropSAM: A Propagation-Based Model for Segmenting Any 3D Objects in Multi-Modal Medical Images | [![arXiv](https://img.shields.io/badge/arXiv-2408.13836-b31b1b.svg)](https://arxiv.org/pdf/2408.13836) | [![GitHub stars](https://img.shields.io/github/stars/czifan/PropSAM.svg?logo=github&label=Stars)](https://github.com/czifan/PropSAM) |
-| Interactive 3d medical image segmentation with sam 2 | [![arXiv](https://img.shields.io/badge/arXiv-2408.02635-b31b1b.svg)](https://arxiv.org/pdf/2408.02635) | [![GitHub stars](https://img.shields.io/github/stars/Chuyun-Shen/SAM_2_Medical_3D.svg?logo=github&label=Stars)](https://github.com/Chuyun-Shen/SAM_2_Medical_3D) |
-| Is SAM 2 Better than SAM in Medical Image Segmentation? | [![arXiv](https://img.shields.io/badge/arXiv-2408.04212-b31b1b.svg)](https://arxiv.org/pdf/2408.04212) |  |
-| SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2408.04593-b31b1b.svg)](https://arxiv.org/pdf/2408.04593) |  |
-| SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More | [![arXiv](https://img.shields.io/badge/arXiv-2408.04579-b31b1b.svg)](https://arxiv.org/pdf/2408.04579) | [![GitHub stars](https://img.shields.io/github/stars/tianrun-chen/SAM-Adapter-PyTorch.svg?logo=github&label=Stars)](https://github.com/tianrun-chen/SAM-Adapter-PyTorch) |
-| Segment Anything in Medical Images and Videos: Benchmark and Deployment | [![arXiv](https://img.shields.io/badge/arXiv-2408.03322-b31b1b.svg)](https://arxiv.org/pdf/2408.03322) | [![GitHub stars](https://img.shields.io/github/stars/bowang-lab/MedSAM.svg?logo=github&label=Stars)](https://github.com/bowang-lab/MedSAM) |
-| Segment anything model 2: an application to 2D and 3D medical images | [![arXiv](https://img.shields.io/badge/arXiv-2408.00756-b31b1b.svg)](https://arxiv.org/pdf/2408.00756) |  |
-| Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2 | [![arXiv](https://img.shields.io/badge/arXiv-2408.01648-b31b1b.svg)](https://arxiv.org/pdf/2408.01648) | [![GitHub stars](https://img.shields.io/github/stars/AngeLouCN/SAM-2_Surgical_Video.svg?logo=github&label=Stars)](https://github.com/AngeLouCN/SAM-2_Surgical_Video) |
-| Medical SAM 2: Segment Medical Images As Video Via Segment Anything Model 2 | [![arXiv](https://img.shields.io/badge/arXiv-2408.00874-b31b1b.svg)](https://arxiv.org/pdf/2408.00874) | [![GitHub stars](https://img.shields.io/github/stars/MedicineToken/Medical-SAM2.svg?logo=github&label=Stars)](https://github.com/MedicineToken/Medical-SAM2) |
-| ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2407.14153-b31b1b.svg)](https://arxiv.org/pdf/2407.14153) |  |
-| Segment Anything in Medical Images | [![arXiv](https://img.shields.io/badge/arXiv-2304.12306-b31b1b.svg)](https://arxiv.org/pdf/2304.12306.pdf) | [![GitHub stars](https://img.shields.io/github/stars/bowang-lab/MedSAM.svg?logo=github&label=Stars)](https://github.com/bowang-lab/MedSAM) |
-| Segment Anything Model for Medical Image Analysis: An Experimental Study | [![arXiv](https://img.shields.io/badge/arXiv-2304.10517-b31b1b.svg)](https://arxiv.org/pdf/2304.10517.pdf) | [![GitHub stars](https://img.shields.io/github/stars/mazurowski-lab/segment-anything-medical.svg?logo=github&label=Stars)](https://github.com/mazurowski-lab/segment-anything-medical) |
-| Segment Anything Model for Medical Images? | [![arXiv](https://img.shields.io/badge/arXiv-2304.14660-b31b1b.svg)](https://arxiv.org/pdf/2304.14660.pdf) | [![GitHub stars](https://img.shields.io/github/stars/yuhoo0302/Segment-Anything-Model-for-Medical-Images.svg?logo=github&label=Stars)](https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images) |
-| Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2304.12620-b31b1b.svg)](https://arxiv.org/pdf/2304.12620.pdf) | [![GitHub stars](https://img.shields.io/github/stars/KidsWithTokens/Medical-SAM-Adapter.svg?logo=github&label=Stars)](https://github.com/KidsWithTokens/Medical-SAM-Adapter) |
-| DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2306.00499-b31b1b.svg)](https://arxiv.org/pdf/2306.00499.pdf) | [![GitHub stars](https://img.shields.io/github/stars/yifangao112/DeSAM.svg?logo=github&label=Stars)](https://github.com/yifangao112/DeSAM) |
-| 3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Medical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2306.13465-b31b1b.svg)](https://arxiv.org/pdf/2306.13465.pdf) | [![GitHub stars](https://img.shields.io/github/stars/med-air/3DSAM-adapter.svg?logo=github&label=Stars)](https://github.com/med-air/3DSAM-adapter) |
-| MedLSAM: Localize and Segment Anything Model for Medical Images | [![arXiv](https://img.shields.io/badge/arXiv-2306.14752-b31b1b.svg)](https://arxiv.org/pdf/2306.14752.pdf) | [![GitHub stars](https://img.shields.io/github/stars/openmedlab/MedLSAM.svg?logo=github&label=Stars)](https://github.com/openmedlab/MedLSAM) |
-| SurgicalSAM: Efficient Class Promptable Surgical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2308.08746-b31b1b.svg)](https://arxiv.org/pdf/2308.08746.pdf) | [![GitHub stars](https://img.shields.io/github/stars/wenxi-yue/SurgicalSAM.svg?logo=github&label=Stars)](https://github.com/wenxi-yue/SurgicalSAM) |
-| SAM-Med2D | [![arXiv](https://img.shields.io/badge/arXiv-2308.16184-b31b1b.svg)](https://arxiv.org/pdf/2308.16184.pdf) | [![GitHub stars](https://img.shields.io/github/stars/OpenGVLab/SAM-Med2D.svg?logo=github&label=Stars)](https://github.com/OpenGVLab/SAM-Med2D) |
-| SAM3D: Segment Anything Model in Volumetric Medical Images | [![arXiv](https://img.shields.io/badge/arXiv-2309.03493-b31b1b.svg)](https://arxiv.org/pdf/2309.03493.pdf) | [![GitHub stars](https://img.shields.io/github/stars/UARK-AICV/SAM3D.svg?logo=github&label=Stars)](https://github.com/UARK-AICV/SAM3D) |
-| SAMUS: Adapting Segment Anything Model for Clinical Ultrasound | [![arXiv](https://img.shields.io/badge/arXiv-2309.06824-b31b1b.svg)](https://arxiv.org/pdf/2309.06824.pdf) | [![GitHub stars](https://img.shields.io/github/stars/xianlin7/SAMUS.svg?logo=github&label=Stars)](https://github.com/xianlin7/SAMUS) |
-| MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Images | [![arXiv](https://img.shields.io/badge/arXiv-2309.08842-b31b1b.svg)](https://arxiv.org/pdf/2309.08842.pdf) | [![GitHub stars](https://img.shields.io/github/stars/cchen-cc/MA-SAM.svg?logo=github&label=Stars)](https://github.com/cchen-cc/MA-SAM) |
-| SAM-Med3D | [![arXiv](https://img.shields.io/badge/arXiv-2310.15161-b31b1b.svg)](https://arxiv.org/pdf/2310.15161.pdf) | [![GitHub stars](https://img.shields.io/github/stars/uni-medical/SAM-Med3D.svg?logo=github&label=Stars)](https://github.com/uni-medical/SAM-Med3D) |
-| ProMISe: Prompt-driven 3D Medical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2310.19721-b31b1b.svg)](https://arxiv.org/pdf/2310.19721.pdf) | [![GitHub stars](https://img.shields.io/github/stars/MedICL-VU/ProMISe.svg?logo=github&label=Stars)](https://github.com/MedICL-VU/ProMISe) |
-| ScribblePrompt: Fast and Flexible Interactive 3D Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2312.07381-b31b1b.svg)](https://arxiv.org/pdf/2312.07381.pdf) | [![GitHub stars](https://img.shields.io/github/stars/halleewong/ScribblePrompt.svg?logo=github&label=Stars)](https://github.com/halleewong/ScribblePrompt) |
-| SemiSAM: Exploring SAM for Enhancing Semi-Supervised Medical Image Segmentation | [![arXiv](https://img.shields.io/badge/arXiv-2312.06316-b31b1b.svg)](https://arxiv.org/pdf/2312.06316.pdf) |  |
-| Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions | [![arXiv](https://img.shields.io/badge/arXiv-2401.03495-b31b1b.svg)](https://arxiv.org/pdf/2401.03495.pdf) | [![GitHub stars](https://img.shields.io/github/stars/YichiZhang98/SAM4MIS.svg?logo=github&label=Stars)](https://github.com/YichiZhang98/SAM4MIS) |
+| Title                                                        | Paper                                                        | Repo                                                         |
+| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| 3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation | [Paper](https://arxiv.org/pdf/2306.13465)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/med-air/3DSAM-adapter) |
+| AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation | [Paper](https://export.arxiv.org/pdf/2308.03726v1.pdf)       | ![GitHub Repo stars](https://img.shields.io/github/stars/JayParanjape/biastuning) |
+| AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation | [Paper](https://arxiv.org/abs/2308.14936v3)                  |                                                              |
+| AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder | [Paper](https://arxiv.org/abs/2306.06370)                    |                                                              |
+| Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting | [Paper](https://arxiv.org/pdf/2309.06824)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/xianlin7/SAMUS) |
+| Biomedical SAM 2: Segment Anything in Biomedical Images and Videos | [Paper](https://arxiv.org/abs/2408.03286)                    |                                                              |
+| Boosting Medical Image Classification with Segmentation Foundation Model | [Paper](https://arxiv.org/abs/2406.11026)                    |                                                              |
+| Can SAM Segment Polyps?                                      | [Paper](https://arxiv.org/pdf/2304.07583.pdf)                |                                                              |
+| CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation | [Paper](https://arxiv.org/abs/2408.00181)                    |                                                              |
+| Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets | [Paper](https://arxiv.org/abs/2304.09324)                    |                                                              |
+| DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation | [Paper](https://arxiv.org/pdf/2306.00499)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/yifangao112/DeSAM) |
+| Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation | [Paper](https://arxiv.org/pdf/2304.12637v2.pdf)              |                                                              |
+| Interactive 3D Medical Image Segmentation with SAM 2         | [Paper](https://arxiv.org/pdf/2408.02635)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/Chuyun-Shen/SAM_2_Medical_3D) |
+| Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection | [Paper](https://arxiv.org/abs/2408.14847)                    |                                                              |
+| Is SAM 2 Better than SAM in Medical Image Segmentation?      | [Paper](https://arxiv.org/pdf/2408.04212)                    |                                                              |
+| MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation | [Paper](https://arxiv.org/abs/2309.08842)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/cchen-cc/MA-SAM) |
+| Medical SAM 2: Segment medical images as video via Segment Anything Model 2 | [Paper](https://arxiv.org/abs/2408.00874)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/MedicineToken/Medical-SAM2) |
+| Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation | [Paper](https://arxiv.org/abs/2304.12620)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/WuJunde/Medical-SAM-Adapter) |
+| MedLSAM: Localize and Segment Anything Model for 3D CT Images | [Paper](https://arxiv.org/pdf/2306.14752)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/openmedlab/MedLSAM) |
+| nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance | [Paper](https://arxiv.org/pdf/2309.16967)                    |                                                              |
+| Polyp SAM 2: Advancing Zero shot Polyp Segmentation in Colorectal Cancer Detection | [Paper](https://arxiv.org/abs/2408.05892)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/sajjad-sh33/Polyp-SAM-2) |
+| Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation? | [Paper](https://arxiv.org/abs/2308.06623v1)                  |                                                              |
+| PROMISE: PROMPT-DRIVEN 3D MEDICAL IMAGE SEGMENTATION USING PRETRAINED IMAGE FOUNDATION MODELS | [Paper](https://arxiv.org/abs/2310.19721)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/MedICL-VU/ProMISe) |
+| PropSAM: A Propagation-Based Model for Segmenting Any 3D Objects in Multi-Modal Medical Images | [Paper](https://arxiv.org/pdf/2408.13836)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/czifan/PropSAM) |
+| SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation | [Paper](https://arxiv.org/pdf/2408.04593)                    |                                                              |
+| SAM Fails to Segment Anything? – SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More | [Paper](https://arxiv.org/pdf/2304.09148.pdf)                | ![GitHub Repo stars](https://img.shields.io/github/stars/tianrun-chen/SAM-Adapter-PyTorch) |
+| SAM Medical Imaging                                          |                                                              | ![GitHub Repo stars](https://img.shields.io/github/stars/amine0110/SAM-Medical-Imaging) |
+| SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning | [Paper](https://arxiv.org/pdf/2304.04738.pdf)                |                                                              |
+| SAM-Med2D                                                    | [Paper](https://arxiv.org/abs/2308.16184)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/OpenGVLab/SAM-Med2D) |
+| SAM-Med3D                                                    | [Paper](https://arxiv.org/abs/2310.15161)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/uni-medical/sam-med3d) |
+| SAM-UNet: Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images | [Paper](https://arxiv.org/pdf/2408.09886)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/Hhankyangg/sam-unet) |
+| SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model | [Paper](https://arxiv.org/pdf/2304.05396.pdf)                |                                                              |
+| SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More | [Paper](https://arxiv.org/pdf/2408.04579)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/tianrun-chen/SAM-Adapter-PyTorch) |
+| SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation | [Paper](https://arxiv.org/pdf/2408.08870)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/WZH0120/SAM2-UNet) |
+| SAM3D: SEGMENT ANYTHING MODEL IN VOLUMETRIC MEDICAL IMAGES   | [Paper](https://arxiv.org/abs/2309.03493)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/UARK-AICV/SAM3D) |
+| SAMM (SEGMENT ANY MEDICAL MODEL): A 3D SLICER INTEGRATION TO SAM | [Paper](https://arxiv.org/pdf/2304.05622.pdf)                | ![GitHub Repo stars](https://img.shields.io/github/stars/bingogome/samm) |
+| SAMMed : A medical image annotation framework based on large vision model | [Paper](https://export.arxiv.org/pdf/2307.05617v2.pdf)       |                                                              |
+| ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image | [Paper](https://arxiv.org/abs/2312.07381)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/aleemsidra/SaLIP) |
+| Segment Anything in Medical Images                           | [Paper](https://www.nature.com/articles/s41467-024-44824-z.pdf) | ![GitHub Repo stars](https://img.shields.io/github/stars/bowang-lab/MedSAM) |
+| Segment Anything in Medical Images and Videos: Benchmark and Deployment | [Paper](https://arxiv.org/abs/2408.03322)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/bowang-lab/MedSAM) |
+| Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging | [Paper](https://arxiv.org/pdf/2304.04155.pdf)                |                                                              |
+| Segment Anything Model (SAM) in Napari                       |                                                              | ![GitHub Repo stars](https://img.shields.io/github/stars/MIC-DKFZ/napari-sam) |
+| Segment Anything Model for Brain Tumor Segmentation          | [Paper](https://arxiv.org/pdf/2309.08434)                    |                                                              |
+| Segment Anything Model for Medical Image Analysis: an Experimental Study | [Paper](https://arxiv.org/pdf/2304.10517.pdf)                |                                                              |
+| Segment-Anything-Automatically-on-Medical-Image (SAAMI)      |                                                              | ![GitHub Repo stars](https://img.shields.io/github/stars/AxDante/SAAMI) |
+| Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning | [Paper](https://arxiv.org/abs/2308.16466v3)                  | ![GitHub Repo stars](https://img.shields.io/github/stars/dragondescentzerotsu/ssm-sam) |
+| Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning | [Paper](https://arxiv.org/abs/2408.07931)                    | ![GitHub Repo stars](https://img.shields.io/github/stars/jinlab-imvr/surgical-sam-2) |
+| SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation Authors | [Paper](https://arxiv.org/abs/2308.08746v2)                  | ![GitHub Repo stars](https://img.shields.io/github/stars/wenxi-yue/SurgicalSAM) |
+| The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning | [Paper](https://arxiv.org/abs/2304.07875)                    |                                                              |
+| When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation | [Paper](https://arxiv.org/pdf/2304.08506v1.pdf)              |                                                              |
+| Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging | [Paper](https://arxiv.org/pdf/2408.06170)                    |                                                              |
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From 25531ae5b1f33311adf445533240b0b9e4540290 Mon Sep 17 00:00:00 2001
From: JefferyJiang-YF <60686137+JefferyJiang-YF@users.noreply.github.com>
Date: Tue, 10 Sep 2024 18:43:41 +0800
Subject: [PATCH 2/2] Add .gitignore to exclude .DS_Store files

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..496ee2c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.DS_Store
\ No newline at end of file