ArticleInternational journal of computer assisted radiology and surgery2019
GTCreator: a flexible annotation tool for image-based datasets.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03775811 (In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy), which is not on this map. Cited by 9 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.
In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy
Who cites it
9 citing papers in PubMed.
- Article
- A complete benchmark for polyp detection, segmentation and classification in colonoscopy images.Frontiers in oncology · 2024Article
- Clinical Validation Benchmark Dataset and Expert Performance Baseline for Colorectal Polyp Localization Methods.Journal of imaging · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Annotation Tools in Gastrointestinal Polyp Annotation.Diagnostics (Basel, Switzerland) · 2022Review
- In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy.Endoscopy international open · 2022Article
- Towards a better understanding of annotation tools for medical imaging: a survey.Multimedia tools and applications · 2022Article
- Unravelling the effect of data augmentation transformations in polyp segmentation.International journal of computer assisted radiology and surgery · 2020Article
- Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study.Journal of imaging · 2020Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeMethodology evaluation for decision support systems for health is a time-consuming task. To assess performance of polyp detection methods in colonoscopy videos, clinicians have to deal with the annotation of thousands of images. Current existing tools could be improved in terms of flexibility and ease of use.
methodsWe introduce GTCreator, a flexible annotation tool for providing image and text annotations to image-based datasets. It keeps the main basic functionalities of other similar tools while extending other capabilities such as allowing multiple annotators to work simultaneously on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets.
resultsThe comparison with other similar tools shows that GTCreator allows to obtain fast and precise annotation of image datasets, being the only one which offers full annotation editing and browsing capabilites.
conclusionOur proposed annotation tool has been proven to be efficient for large image dataset annotation, as well as showing potential of use in other stages of method evaluation such as experimental setup or results analysis.
Indexed as
Identifiers
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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.