AIMC Topic: Imaging, Three-Dimensional

Clear Filters Showing 1041 to 1050 of 1894 articles

Deep learning-based digitization of prostate brachytherapy needles in ultrasound images.

Medical physics
PURPOSE: To develop, and evaluate the performance of, a deep learning-based three-dimensional (3D) convolutional neural network (CNN) artificial intelligence (AI) algorithm aimed at finding needles in ultrasound images used in prostate brachytherapy.

CS-Net: Deep learning segmentation of curvilinear structures in medical imaging.

Medical image analysis
Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the...

3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis.

NPJ systems biology and applications
During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To ac...

Advancements in sex estimation using the diaphyseal cross-sectional geometric properties of the lower and upper limbs.

International journal of legal medicine
This paper introduces an automated method for estimating sex from the lower and upper limbs based on diaphyseal CSG properties. The proposed method was developed and evaluated using 389 femurs, 412 tibias, and 404 humeri of adult individuals from a m...

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

EBioMedicine
BACKGROUND: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC).

Efficient and Robust Instrument Segmentation in 3D Ultrasound Using Patch-of-Interest-FuseNet with Hybrid Loss.

Medical image analysis
Instrument segmentation plays a vital role in 3D ultrasound (US) guided cardiac intervention. Efficient and accurate segmentation during the operation is highly desired since it can facilitate the operation, reduce the operational complexity, and the...

Learning patterns of the ageing brain in MRI using deep convolutional networks.

NeuroImage
Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning mo...

Automated rotator cuff tear classification using 3D convolutional neural network.

Scientific reports
Rotator cuff tear (RCT) is one of the most common shoulder injuries. When diagnosing RCT, skilled orthopedists visually interpret magnetic resonance imaging (MRI) scan data. For automated and accurate diagnosis of RCT, we propose a full 3D convolutio...

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis.

Scientific reports
Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to ...

Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging.

Physics in medicine and biology
The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enha...