ArticleQuantitative imaging in medicine and surgery2012
A survey of GPU-based medical image computing techniques.
Article in Quantitative imaging in medicine and surgery, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Predictive models for intestinal obstruction: from clinical scores to artificial intelligence.Frontiers in surgery · 2026Review
- A highly efficient, scalable pipeline for fixed feature extraction from large-scale high-content imaging screens.iScience · 2024Article
- Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods.Sensors (Basel, Switzerland) · 2024Article
- Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network.International journal of environmental research and public health · 2023Article
- Differential Deep Convolutional Neural Network Model for Brain Tumor Classification.Brain sciences · 2021Article
- Pancreatic cancer detection and characterization-state of the art cross-sectional imaging and imaging data analysis.Translational gastroenterology and hepatology · 2019Review
- A survey of GPU-based acceleration techniques in MRI reconstructions.Quantitative imaging in medicine and surgery · 2018Review
- Robust Cell Detection for Large-Scale 3D Microscopy Using GPU-Accelerated Iterative Voting.Frontiers in neuroanatomy · 2018Article
- The Utility of Cloud Computing in Analyzing GPU-Accelerated Deformable Image Registration of CT and CBCT Images in Head and Neck Cancer Radiation Therapy.IEEE journal of translational engineering in health and medicine · 2016Article
- Imalytics Preclinical: Interactive Analysis of Biomedical Volume Data.Theranostics · 2016Article
- Survey statistics of automated segmentations applied to optical imaging of mammalian cells.BMC bioinformatics · 2015Article
- Recent advances in surgical planning & navigation for tumor biopsy and resection.Quantitative imaging in medicine and surgery · 2015Article
- Accelerating Neuroimage Registration through Parallel Computation of Similarity Metric.PloS one · 2015Article
- GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.PloS one · 2015Article
- GPU accelerated segmentation and centerline extraction of tubular structures from medical images.International journal of computer assisted radiology and surgery · 2014Article
- Toward real-time availability of 3D temperature maps created with temporally constrained reconstruction.Magnetic resonance in medicine · 2014Article
- Enabling large-scale biomedical analysis in the cloud.BioMed research international · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications. The major purpose of this survey is to provide a comprehensive reference source for the starters or researchers involved in GPU-based medical image processing. Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.