ArticleScientific reports2024
An efficient segment anything model for the segmentation of medical images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network.Scientific reports · 2026Article
- SynSAM: a hybrid synchronous learning framework with knowledge retention for prostate zonal segmentation leveraging the segment anything model.Medical & biological engineering & computing · 2026Article
- StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT.NPJ digital medicine · 2026Article
- Anatomically-guided Masked Autoencoder with Domain-Adaptive Prompting (AMAP) for multimodal cerebral aneurysm detection and segmentation.NPJ digital medicine · 2025Article
- MHAU-Net: a multi-scale hybrid attention U-shaped network for the segmentation of MRI breast tumors.Quantitative imaging in medicine and surgery · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
This paper introduces the efficient medical-images-aimed segment anything model (EMedSAM), addressing the high computational demands and limited adaptability of using SAM for medical image segmentation tasks. We present a novel, compact image encoder, DD-TinyViT, designed to enhance segmentation efficiency through an innovative parameter tuning method called med-adapter. The lightweight DD-TinyViT encoder is derived from the well-known ViT-H using a decoupled distillation approach.The segmentation and recognition capabilities of EMedSAM for specific structures are improved by med-adapter, which dynamically adjusts the model parameters specifically for medical imaging. We conducted extensive testing on EMedSAM using the public FLARE 2022 dataset and datasets from the First Hospital of Zhejiang University School of Medicine. The results demonstrate that our model outperforms existing state-of-the-art models in both multi-organ and lung segmentation tasks.
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Registered trials
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