Recent technical innovations have made MR imaging a useful technique for imaging the pancreas. The potential impact of MR imaging on the management and outcome of cases can be determined only by controlled prospective comparative studies; however, these cannot be performed adequately until the normal and abnormal appearances of the pancreas on state-of-the-art MR images are understood. This pictorial essay is presented to further this intermediate goal.
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http://dx.doi.org/10.2214/ajr.159.3.1503018 | DOI Listing |
Health Inf Sci Syst
December 2025
School of Mathematics and Computing, University of Southern Queensland, 487-535 West Street, Toowoomba, QLD 4350 Australia.
Purpose: This paper aims to develop a three-dimensional (3D) Alzheimer's disease (AD) prediction method, thereby bettering current predictive methods, which struggle to fully harness the potential of structural magnetic resonance imaging (sMRI) data.
Methods: Traditional convolutional neural networks encounter pressing difficulties in accurately focusing on the AD lesion structure. To address this issue, a 3D decoupling, self-attention network for AD prediction is proposed.
Med Image Comput Comput Assist Interv
September 2022
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus.
Quantitative evaluation of pediatric craniofacial anomalies relies on the accurate identification of anatomical landmarks and structures. While segmentation and landmark detection methods in standard clinical images are available in the literature, image-based methods are not directly applicable to 3D photogrammetry because of its unstructured nature consisting in variable numbers of vertices and polygons. In this work, we propose a graph-based convolutional neural network based on Chebyshev polynomials that exploits vertex coordinates, polygonal connectivity, and surface normal vectors to extract multi-resolution spatial features from the 3D photographs.
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January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
View Article and Find Full Text PDFFront Artif Intell
January 2025
CONAHCYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, Mexico.
This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico in diverse fields. These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. This review article aimed to provide comprehensive information on the Machine Learning and Deep Learning algorithms applied in Mexico.
View Article and Find Full Text PDFFront Cell Dev Biol
January 2025
Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo, China.
Background: Vessel segmentation in fundus photography has become a cornerstone technique for disease analysis. Within this field, Ultra-WideField (UWF) fundus images offer distinct advantages, including an expansive imaging range, detailed lesion data, and minimal adverse effects. However, the high resolution and low contrast inherent to UWF fundus images present significant challenges for accurate segmentation using deep learning methods, thereby complicating disease analysis in this context.
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