ArticleProceedings of SPIE--the International Society for Optical Engineering2024
FNPC-SAM: Uncertainty-Guided False Negative/Positive Control for SAM on Noisy Medical Images.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- VFMStitch: A Vision-Foundation-Model Empowered Framework for 3D Ultrasound Stitching via Geometric-Semantic Feature Fusion.Proceedings of machine learning research · 2026Article
- From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- SEGMENTATION CONFIDENCE FOR ARBITRARY CNNS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
- SYNSTITCH: A SELF-SUPERVISED LEARNING NETWORK FOR ULTRASOUND IMAGE STITCHING USING SYNTHETIC TRAINING PAIRS AND INDIRECT SUPERVISION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- WS-SfMLearner: self-supervised monocular depth and ego-motion estimation on surgical videos with unknown camera parameters.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
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Authors and funding
12 authors.
Funding
Abstract
The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code has been released at: https://github.com/MedICL-VU/FNPC-SAM.
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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.