AJR Am J Roentgenol
Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave., M-391, San Francisco, CA 94143, USA.
Published: December 2012
Objective: The purpose of this article is to discuss how to convert cross-sectional images into a 3D model and embed them in a Portable Document Format (PDF) file. Four programs are used: OsiriX, MeshLab, Microsoft PowerPoint, and Adobe Acrobat. Step-by-step instructions are provided.
Conclusion: Embedding 3D radiology models into PDF files is a powerful tool that may be used for clinical, educational, and research purposes.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2214/AJR.12.8716 | DOI Listing |
Clin Oral Investig
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
Objectives: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
Materials And Methods: 175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening.
Diagnostics (Basel)
January 2025
Aerospace Engineering Department and Interdisciplinary Research Center for Smart Mobility and Logistics, and Interdisciplinary Research Center Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. Diagnostic radiology is no exception.
View Article and Find Full Text PDFBiosensors (Basel)
January 2025
Department of Bioscience and Biotechnology, Konkuk University, Seoul 05029, Republic of Korea.
Lateral flow immunoassays (LFIAs) are widely used for their low cost, simplicity, and rapid results; however, enhancing their reliability requires the meticulous selection of ligands and nanoparticles (NPs). SiO@QD@SiO (QD) nanoparticles, which consist of quantum dots (QDs) embedded in a silica (SiO) core and surrounded by an outer SiO shell, exhibit significantly higher fluorescence intensity (FI) compared to single QDs. In this study, we prepared QD@PEG@Aptamer, an aptamer conjugated with QD using succinimidyl-[(N-maleimidopropionamido)-hexaethyleneglycol]ester, which is 130 times brighter than single QDs, for detecting carbohydrate antigen (CA) 19-9 through LFIA.
View Article and Find Full Text PDFPathophysiology
January 2025
Division of Anatomical Pathology, Department of Pathology, College of Medicine, University of Saskatchewan, Royal University Hospital, 103 Hospital Drive, Saskatoon, SK S7N 0W8, Canada.
: Cause of death analysis is fundamental to forensic pathology. We present the case of a 9½-year-old girl with a genetically confirmed diagnosis of Dravet syndrome who died in her sleep with no evidence of motor seizure. She also had a lifelong history of recurrent pneumonias and, along with her family, had tested positive for COVID-19 10 days before death.
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Southern Medical University, Guangzhou, China. Electronic address:
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the recent proliferation of foundation models (FMs) for patch-level embedding and the diversity of slide-level aggregations. This paper implemented and systematically compared six FMs and six recent MIL methods by organizing different feature extractions and aggregations across seven clinically relevant end-to-end prediction tasks using WSIs from 4044 patients with four different cancer types.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!
© LitMetric 2025. All rights reserved.