ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2017
CT Image Enhancement for Feature Detection and Localization.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Deep learning toolbox for automated enhancement, segmentation, and graphing of cortical optical coherence tomography microangiograms.Biomedical optics express · 2020Article
- An open-source framework for pulmonary fissure completeness assessment.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2020Article
- Generative-based airway and vessel morphology quantification on chest CT images.Medical image analysis · 2020Article
- Bronchial Cartilage Assessment with Model-Based GAN Regressor.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
- A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.Medical image analysis · 2019Article
- Imaging Advances in Chronic Obstructive Pulmonary Disease. Insights from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) Study.American journal of respiratory and critical care medicine · 2019Review
- Accurate Measurement of Airway Morphology on Chest CT Images.Image analysis for moving organ, breast, and thoracic images : third International Workshop, RAMBO 2018, fourth International Workshop, BIA 2018, and first International Workshop, TIA 2018, held in conjunction with MICCAI 2018, Granada,... · 2018Article
- Artificial Intelligence in COPD: New Venues to Study a Complex Disease.Barcelona respiratory network reviewsArticle
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
Abstract
In recent years, many pre-processing filters have been developed in order to enhance anatomical structures on chest CT images. These filters are typically based on the analysis of the multiscale second-order local information of the image, that helps identify structures with even (tubes) or odd (surfaces) symmetries. Therefore, they often require specific parameter tuning to enhance the different structures. Moreover, while the filters seem to be able to isolate the structure of interest, they do not provide information about the sub-voxel location of the feature. In this work, we present a novel method for vessel, airway, and fissure strength computation on chest CT images using convolutional neural networks. A scale-space particle segmentation is used to isolate training points for vessels, airways, and fissures which are then used to train an 8-layer neural network with 3 convolutional layers which define high order local information of the image. The network returns a probability map of each feature and provides information on the feature offset from the voxel sampling center, allowing for sub-voxel location of the different structures. The proposed method has been evaluated on clinical CT images and compared to other methods for feature enhancement available in the literature. Results show that the proposed method outperforms competing algorithms in terms of enhancement and is also unique in providing subvoxel information.
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.