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Segment Anything in Medical Images and Videos: Benchmark and Deployment
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Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical data remains unclear. In this work, we first present a comprehensive benchmarking of the Segment Anything Model 2 (SAM2) across 11 medical image modalities and videos and point out its strengths and weaknesses by comparing it to SAM1 and MedSAM. Then, we develop a transfer learning pipeline and demonstrate SAM2 can be quickly adapted to medical domain by fine-tuning. Furthermore, we implement SAM2 as a 3D slicer plugin and Gradio API for efficient 3D image and video segmentation. The code has been made publicly available at \url{https://github.com/bowang-lab/MedSAM}.
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Cited by 9 Pith papers
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MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation
Routing a frozen expert's prior through a frozen promptable foundation model with multi-prompt fusion and a plausibility guard raises median Dice from 0.71 to 0.92 on hip MRI and 0.89 to 0.92 on shoulder CT, without a...
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SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens
Freezing SAM2's encoder, fine-tuning its decoder/memory, and adding TSDF-trained global volume tokens yields 0.78 mean Dice on a new 34-dataset MRI benchmark, up from 0.58 zero-shot.
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Organoid Tracker: A SAM2-Powered Platform for Zero-shot Cyst Analysis in Human Kidney Organoid Videos
A SAM2-based open-source GUI with inverse temporal tracking for quantifying kidney organoid cyst growth in bright-field videos, demonstrated on two videos with noted early-frame failures.
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Promptable cancer segmentation using minimal expert-curated data
A two-classifier guided spiral search enables prostate cancer segmentation from a single point prompt using only 32 training images.
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Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation
Enhances MedSAM with a 1.6M-parameter Box Predictor trained in two stages to convert single clicks to bounding boxes, reporting Dice scores of 0.89-0.98 on four medical datasets across CT, MRI, and ultrasound.
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Segment Anything for Cell Tracking
SAM2-based, annotation-free cell tracking links cells and detects divisions in 2D and 3D time-lapse videos, achieving top-3 linking accuracy on Cell Tracking Challenge benchmarks.
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FreeVPS: Repurposing Training-Free SAM2 for Generalizable Video Polyp Segmentation
FreeVPS pairs a per-frame polyp segmenter with frozen SAM2 tracking and two filtering modules to reduce error accumulation, improving in-domain and out-of-domain video polyp segmentation.
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Auto-nnU-Net: Towards Automated Medical Image Segmentation
Auto-nnU-Net, an AutoML extension of nnU-Net, reports the highest mean test Dice score on the Medical Segmentation Decathlon while optimizing both accuracy and training runtime.
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A Comprehensive Pipeline for Aortic Segmentation and Shape Analysis
An MRI aortic pipeline with nnUNet segmentation, mesh reconstruction, and gradient-descent registration produces a 599-subject healthy shape model with reported PCA modes.
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