PURPOSE. To describe and evaluate the performance of a computerized automated segmentation technique for use in quantification of the foveal avascular zone (FAZ). METHODS. A computerized technique for automated segmentation of the FAZ using images from fundus fluorescein angiography (FFA) was applied to 26 transit-phase images obtained from patients with various grades of diabetic retinopathy. The area containing the FAZ zone was first extracted from the original image and smoothed by a Gaussian kernel (sigma = 1.5). An initializing contour was manually placed inside the FAZ of the smoothed image and iteratively moved by the segmentation program toward the FAZ boundary. Five tests with different initializing curves were run on each of 26 images to assess reproducibility. The accuracy of the program was also validated by comparing results obtained by the program with the FAZ boundaries manually delineated by medical retina specialists. Interobserver performance was then evaluated by comparing delineations from two of the experts. RESULTS. One-way analysis of variance indicated that the disparities between different tests were not statistically significant, signifying excellent reproducibility for the computer program. There was a statistically significant linear correlation between the results obtained by automation and manual delineations by experts. CONCLUSIONS. This automated segmentation program can produce highly reproducible results that are comparable to those made by clinical experts. It has the potential to assist in the detection and management of foveal ischemia and to be integrated into automated grading systems.
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http://dx.doi.org/10.1167/iovs.09-4935 | DOI Listing |
Sci Rep
January 2025
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.
Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing.
View Article and Find Full Text PDFAm J Orthod Dentofacial Orthop
February 2025
Department of Orthodontics, Faculty of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.
J Shoulder Elbow Surg
January 2025
Department of Orthopedics and Trauma, Peking University People's Hospital, Beijing 100044, China; Key Laboratory of Trauma and Neural Regeneration (Peking University), Ministry of Education, Beijing 100044, China; National Center for Trauma Medicine, Peking University People's Hospital, Beijing 100044, China. Electronic address:
Objective: The bare area is defined as a transverse region within the trochlear notch, serving as an optimal entry point for olecranon osteotomy due to the absence of articular cartilage coverage. However, there is limited research on the morphology and location of the bare area, and there is a lack of intuitive visual description. Thus, the purpose of this study is to delineate anatomical features of the bare area and visualize its morphology and refine the olecranon osteotomy approach.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Architectural Engineering, Dankook University, 152 Jukjeon-ro, Yongin-si 16890, Republic of Korea.
In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Civil Engineering, The University of Mississippi, University, MS 38677, USA.
The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and roadway geometry data from an automated shuttle pilot program, from July to December 2023, at the University of North Carolina in Charlotte, were collected. The automated shuttle uses sensors for localization, navigation, and obstacle detection.
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