ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2024
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- Adapting general representations of pretrained vision foundation models to seismic understanding.Communications engineering · 2026Article
- High-frequency energy fusion (HFEF) for nuclei segmentation with boundary-aware loss.Quantitative imaging in medicine and surgery · 2026Article
- Depthwise-Dilated Convolutional Adapters for Medical Object Tracking and Segmentation Using the Segment Anything Model 2.Machine learning: science and technology · 2025Article
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.Abdominal radiology (New York) · 2025Review
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- CDA-mamba: cross-directional attention mamba for enhanced 3D medical image segmentation.Scientific reports · 2025Article
- A segment anything model-guided and match-based semi-supervised segmentation framework for medical imaging.Medical physics · 2025Article
- Intelligent control of robotic X-ray devices using a language-promptable digital twin.International journal of computer assisted radiology and surgery · 2025Article
- Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.Emergency radiology · 2025Article
- ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- ProtoSAM-2D: 2D Semantic Segment Anything Model with Mask-Level Prototype-Learning and Distillation.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Evaluation of large language model-driven AutoML in data and model management from human-centered perspective.Frontiers in artificial intelligence · 2025Article
- A two-stage deep learning prediction system for colon cancer microsatellite instability status using CT images.Frontiers in oncology · 2025Article
- FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification.Foundation models for general medical AI : second international workshop, MedAGI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, proceedings. MedAGI (Workshop) (2nd : 2024 : Marrakech, Morocco) · 2025Article
- Customized SAM-Med3D with multi-view adapter and T2-FLAIR mismatch features for glioma IDH genotyping and grading.Frontiers in behavioral neuroscience · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly prompted. Interactivity is a key strength of SAMs, allowing users to iteratively provide prompts that specify objects of interest to refine outputs. However, to realize the interactive use of SAMs for 3D medical imaging tasks, rapid inference times are necessary. High memory requirements and long processing delays remain constraints that hinder the adoption of SAMs for this purpose. Specifically, while 2D SAMs applied to 3D volumes contend with repetitive computation to process all slices independently, 3D SAMs suffer from an exponential increase in model parameters and FLOPS. To address these challenges, we present FastSAM3D which accelerates SAM inference to 8 milliseconds per 128 × 128 × 128 3D volumetric image on an NVIDIA A100 GPU. This speedup is accomplished through 1) a novel layer-wise progressive distillation scheme that enables knowledge transfer from a complex 12-layer ViT-B to a lightweight 6-layer ViT-Tiny variant encoder without training from scratch; and 2) a novel 3D sparse flash attention to replace vanilla attention operators, substantially reducing memory needs and improving parallelization. Experiments on three diverse datasets reveal that FastSAM3D achieves a remarkable speedup of 527.38× compared to 2D SAMs and 8.75× compared to 3D SAMs on the same volumes without significant performance decline. Thus, FastSAM3D opens the door for low-cost truly interactive SAM-based 3D medical imaging segmentation with commonly used GPU hardware. Code is available at https://github.com/arcadelab/FastSAM3D.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.