Jeffrey Stout | Searcharxiv

arXiv2023

Smoothing spline analysis of variance models: A new tool for the analysis of accelerometer data

Accelerometer data is commonplace in physical activity research, exercise science, and public health studies, where the goal is to understand and compare physical activity differences between groups and/or subject populations, and to identify patterns and trends in physical activity behavior to inform interventions for improving public health. We propose using mixed-effects smoothing spline analysis of variance (SSANOVA) as a new tool for analyzing accelerometer data. By representing data as functions or curves, smoothing spline allows for accurate modeling of the underlying physical activity patterns throughout the day, especially when the accelerometer data is continuous and sampled at high frequency. The SSANOVA framework makes it possible to decompose the estimated function into the portion that is common across groups (i.e., the average activity) and the portion that differs across groups. By decomposing the function of physical activity measurements in such a manner, we can estimate group differences and identify the regions of difference. In this study, we demonstrate the advantages of utilizing SSANOVA models to analyze accelerometer-based physical activity data collected from community-dwelling older adults across various fall risk categories. Using Bayesian confidence intervals, the SSANOVA results can be used to reliably quantify physical activity differences between fall risk groups and identify the time regions that differ throughout the day.

stat.AP↗

arXiv2023

Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes without previously-required re-training or fine-tuning specific to each new dataset. Purpose: To test SAM's accuracy in various medical image segmentation tasks and investigate potential factors that may affect its accuracy in medical images. Methods: SAM was tested on 12 public medical image segmentation datasets involving 7,451 subjects. The accuracy was measured by the Dice overlap between the algorithm-segmented and ground-truth masks. SAM was compared with five state-of-the-art algorithms specifically designed for medical image segmentation tasks. Associations of SAM's accuracy with six factors were computed, independently and jointly, including segmentation difficulties as measured by segmentation ability score and by Dice overlap in U-Net, image dimension, size of the target region, image modality, and contrast. Results: The Dice overlaps from SAM were significantly lower than the five medical-image-based algorithms in all 12 medical image segmentation datasets, by a margin of 0.1-0.5 and even 0.6-0.7 Dice. SAM-Semantic was significantly associated with medical image segmentation difficulty and the image modality, and SAM-Point and SAM-Box were significantly associated with image segmentation difficulty, image dimension, target region size, and target-vs-background contrast. All these 3 variations of SAM were more accurate in 2D medical images, larger target region sizes, easier cases with a higher Segmentation Ability score and higher U-Net Dice, and higher foreground-background contrast.

eess.IV↗