KEYWORDS: Image segmentation, Medical imaging, Kidney, Ultrasonography, Monte Carlo methods, Performance modeling, Image enhancement, Data modeling, Reliability, Uncertainty analysis
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
Purpose: Placental size in early pregnancy has been associated with important clinical outcomes, including fetal growth. However, extraction of placental size from three-dimensional ultrasound (3DUS) requires time-consuming interactive segmentation methods and is prone to user variability. We propose a semiautomated segmentation technique that requires minimal user input to robustly measure placental volume from 3DUS images.
Approach: For semiautomated segmentation, a single, central 2D slice was manually annotated to initialize an automated multi-atlas label fusion (MALF) algorithm. The dataset consisted of 47 3DUS volumes obtained at 11 to 14 weeks in singleton pregnancies (28 anterior and 19 posterior). Twenty-six of these subjects were imaged twice within the same session. Dice overlap and surface distance were used to quantify the automated segmentation accuracy compared to expert manual segmentations. The mean placental volume measurements obtained by our method and VOCAL (virtual organ computer-aided analysis), a leading commercial semiautomated method, were compared to the manual reference set. The test–retest reliability was also assessed.
Results: The overlap between our automated segmentation and manual (mean Dice: 0.824 ± 0.061, median: 0.831) was within the range reported by other methods requiring extensive manual input. The average surface distance was 1.66 ± 0.96 mm. The correlation coefficient between test–retest volumes was r = 0.88, and the intraclass correlation was ICC ( 1 ) = 0.86.
Conclusions: MALF is a promising method that can allow accurate and reliable segmentation of the placenta with minimal user interaction. Further refinement of this technique may allow for placental biometry to be incorporated into clinical pregnancy surveillance.
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