FNPC-SAM: uncertainty-guided false negative/positive control for SAM on noisy medical images

Xing Yao; Han Liu; Dewei Hu; Daiwei Lu; Ange Lou; Hao Li; Ruining Deng; Gabriel Arenas; Baris Oguz; Nadav Schwartz; Brett C. Byram; Ipek Oguz

doi:10.1117/12.3006867

2 April 2024 FNPC-SAM: uncertainty-guided false negative/positive control for SAM on noisy medical images

Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett C. Byram, Ipek Oguz

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292602 (2024) https://doi.org/10.1117/12.3006867
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

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

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett C. Byram, and Ipek Oguz "FNPC-SAM: uncertainty-guided false negative/positive control for SAM on noisy medical images", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292602 (2 April 2024); https://doi.org/10.1117/12.3006867

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