ArticleScientific data2018
A new dataset of computed-tomography angiography images for computer-aided detection of pulmonary embolism.
Article in Scientific data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Accuracy of AI-Based Algorithms in Pulmonary Embolism Detection on Computed Tomographic Pulmonary Angiography: An Updated Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Deep learning-based segmentation of acute pulmonary embolism in cardiac CT images.International journal of computer assisted radiology and surgery · 2026Article
- Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.Journal of imaging informatics in medicine · 2026Article
- AI-Enhanced Deep Learning Framework for Pulmonary Embolism Detection in CT Angiography.Bioengineering (Basel, Switzerland) · 2025Article
- Large-vocabulary segmentation for medical images with text prompts.NPJ digital medicine · 2025Article
- High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ digital medicine · 2025Article
- Detection and segmentation of pulmonary embolism in 3D CT pulmonary angiography using a threshold adjustment segmentation network.Scientific reports · 2025Article
- Machine Learning-Based Objective Evaluation Model of CTPA Image Quality: A Multi-Center Study.International journal of general medicine · 2025Article
- Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography.Heliyon · 2024Article
- PE-Ynet: a novel attention-based multi-task model for pulmonary embolism detection using CT pulmonary angiography (CTPA) scan images.Physical and engineering sciences in medicine · 2024Article
- Cardiac ultrasound simulation for autonomous ultrasound navigation.Frontiers in cardiovascular medicine · 2024Article
- Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism.Medical image analysis · 2024Article
- Automated detection, segmentation and measurement of major vessels and the trachea in CT pulmonary angiography.Scientific reports · 2023Article
- Pixel-level annotated dataset of computed tomography angiography images of acute pulmonary embolism.Scientific data · 2023Article
- Feature-enhanced adversarial semi-supervised semantic segmentation network for pulmonary embolism annotation.Heliyon · 2023Article
- Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform.Journal of digital imaging · 2022Article
- LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.Scientific data · 2021Article
- Assessment of Acute Pulmonary Embolism by Computer-Aided Technique: A Reliability Study.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation.Medical image analysis · 2019Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lack of publicly available datasets of computed-tomography angiography (CTA) images for pulmonary embolism (PE) is a problem felt by physicians and researchers. Although a number of computer-aided detection (CAD) systems have been developed for PE diagnosis, their performance is often evaluated using private datasets. In this paper, we introduce a new public dataset called FUMPE (standing for Ferdowsi University of Mashhad's PE dataset) which consists of three-dimensional PE-CTA images of 35 different subjects with 8792 slices in total. For each benchmark image, two expert radiologists provided the ground-truth with the assistance of a semi-automated image processing software tool. FUMPE is a challenging benchmark for CAD methods because of the large number (i.e., 3438) of PE regions and, more especially, because of the location of most of them (i.e., 67%) in lung peripheral arteries. Moreover, due to the reporting of the Qanadli score for each PE-CTA image, FUMPE is the first public dataset which can be used for the analysis of mortality and morbidity risks associated with PE. We also report some complementary prognosis information for each subject.
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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.