False Negative/Positive Control for SAM on Noisy Medical Images

• URL: http://arxiv.org/abs/2308.10382v1
• Date: Sun, 20 Aug 2023 23:01:46 GMT
• Title: False Negative/Positive Control for SAM on Noisy Medical Images
• Authors: 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
• Abstract summary: The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. Our results allow efficient use of SAM in even noisy, low-contrast medical images.
• Score: 10.654917277821495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 will be released soon.

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