ArticleInternational journal of computer assisted radiology and surgery2014
GPU accelerated segmentation and centerline extraction of tubular structures from medical images.
Article in International journal of computer assisted radiology and surgery, 2014. 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.
- A multimodal image guiding system for Navigated Ultrasound Bronchoscopy (EBUS): A human feasibility study.PloS one · 2017Trial
- A Precise Pulmonary Airway Tree Segmentation Method Using Quasi-Spherical Region Constraint and Tracheal Wall Gap Sealing.Sensors (Basel, Switzerland) · 2024Article
- AeroPath: An airway segmentation benchmark dataset with challenging pathology and baseline method.PloS one · 2024Article
- Hepatic vessels segmentation using deep learning and preprocessing enhancement.Journal of applied clinical medical physics · 2023Article
- Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters.Journal of digital imaging · 2020Review
- AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations.PloS one · 2020Article
- Using the CustusX toolkit to create an image guided bronchoscopy application: Fraxinus.PloS one · 2019Article
- Biomechanics-based graph matching for augmented CT-CBCT.International journal of computer assisted radiology and surgery · 2018Article
- Automatic intraoperative estimation of blood flow direction during neurosurgical interventions.International journal of computer assisted radiology and surgery · 2018Article
- Towards a patient-specific hepatic arterial modeling for microspheres distribution optimization in SIRT protocol.Medical & biological engineering & computing · 2018Article
- A novel platform for electromagnetic navigated ultrasound bronchoscopy (EBUS).International journal of computer assisted radiology and surgery · 2016Article
- CustusX: an open-source research platform for image-guided therapy.International journal of computer assisted radiology and surgery · 2016Review
- FAST: framework for heterogeneous medical image computing and visualization.International journal of computer assisted radiology and surgery · 2015Article
- Online tracking of interventional devices for endovascular aortic repair.International journal of computer assisted radiology and surgery · 2015Article
- Airway Segmentation and Centerline Extraction from Thoracic CT - Comparison of a New Method to State of the Art Commercialized Methods.PloS one · 2015Article
- Ab Externo Imaging of Human Episcleral Vessels Using Fiberoptic Confocal Laser Endomicroscopy.Journal of ophthalmic & vision researchReview
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo create a fast and generic method with sufficient quality for extracting tubular structures such as blood vessels and airways from different modalities (CT, MR and US) and organs (brain, lungs and liver) by utilizing the computational power of graphic processing units (GPUs).
methodsA cropping algorithm is used to remove unnecessary data from the datasets on the GPU. A model-based tube detection filter combined with a new parallel centerline extraction algorithm and a parallelized region growing segmentation algorithm is used to extract the tubular structures completely on the GPU. Accuracy of the proposed GPU method and centerline algorithm is compared with the ridge traversal and skeletonization/thinning methods using synthetic vascular datasets.
resultsThe implementation is tested on several datasets from three different modalities: airways from CT, blood vessels from MR, and 3D Doppler Ultrasound. The results show that the method is able to extract airways and vessels in 3-5 s on a modern GPU and is less sensitive to noise than other centerline extraction methods.
conclusionsTubular structures such as blood vessels and airways can be extracted from various organs imaged by different modalities in a matter of seconds, even for large datasets.
Indexed as
Identifiers
24177985What 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.