ArXiv, Year (2020-3) , Pages (1-8)

Title : ( Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE) )

Authors: German Gonzalez , Daniel Jimenez-Carretero , Sara Rodriguez-Lopez , Carlos Cano-Espinosa , Miguel Cazorla , Tanya Agarwal , Vinit Agarwal , Nima Tajbakhsh , Michael B. Gotway , Jianming Liang , mojtaba masoudi , noushin eftekhari , Mahdi Saadatmand , Hamid Reza Pourreza , Patricia Fraga-Rivas , Eduardo Fraile , Frank J. Rybicki , Ara Kassarjian , Raul San Jose Estepar , Maria J. Ledesma-Carbayo ,

Citation: BibTeX | EndNote

Abstract

Rationale: Computer aided detection (CAD) algorithms for Pulmonary Embolism (PE) algorithms have been shown to increase radiologists\\\' sensitivity with a small increase in specificity. However, CAD for PE has not been adopted into clinical practice, likely because of the high number of false positives current CAD software produces. Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics. Methods: 91 Computed tomography pulmonary angiography scans were annotated by at least one radiologist by segmenting all pulmonary emboli visible on the study. 20 annotated CTPAs were open to the public in the form of a medical image analysis challenge. 20 more were kept for evaluation purposes. 51 were made available post-challenge. 8 submissions, 6 of them novel, were evaluated on the 20 evaluation CTPAs. Performance was measured as per embolus sensitivity vs. false positives per scan curve. Results: The best algorithms achieved a per-embolus sensitivity of 75% at 2 false positives per scan (fps) or of 70% at 1 fps, outperforming the state of the art. Deep learning approaches outperformed traditional machine learning ones, and their performance improved with the number of training cases. Significance: Through this work and challenge we have improved the state-of-the art of computer aided detection algorithms for pulmonary embolism. An open database and an evaluation benchmark for such algorithms have been generated, easing the development of further improvements. Implications on clinical practice will need further research.

Keywords

, Computer Aided Analysis, Pulmonary Embolism, Computer Aided Detection, Database, Deep Leaning
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@article{paperid:1092595,
author = {German Gonzalez and Daniel Jimenez-Carretero and Sara Rodriguez-Lopez and Carlos Cano-Espinosa and Miguel Cazorla and Tanya Agarwal and Vinit Agarwal and Nima Tajbakhsh and Michael B. Gotway and Jianming Liang and Masoudi, Mojtaba and Eftekhari, Noushin and Saadatmand, Mahdi and Pourreza, Hamid Reza and Patricia Fraga-Rivas and Eduardo Fraile and Frank J. Rybicki and Ara Kassarjian and Raul San Jose Estepar and Maria J. Ledesma-Carbayo},
title = {Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)},
journal = {ArXiv},
year = {2020},
month = {March},
issn = {2150-8145},
pages = {1--8},
numpages = {7},
keywords = {Computer Aided Analysis; Pulmonary Embolism; Computer Aided Detection; Database; Deep Leaning},
}

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%0 Journal Article
%T Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)
%A German Gonzalez
%A Daniel Jimenez-Carretero
%A Sara Rodriguez-Lopez
%A Carlos Cano-Espinosa
%A Miguel Cazorla
%A Tanya Agarwal
%A Vinit Agarwal
%A Nima Tajbakhsh
%A Michael B. Gotway
%A Jianming Liang
%A Masoudi, Mojtaba
%A Eftekhari, Noushin
%A Saadatmand, Mahdi
%A Pourreza, Hamid Reza
%A Patricia Fraga-Rivas
%A Eduardo Fraile
%A Frank J. Rybicki
%A Ara Kassarjian
%A Raul San Jose Estepar
%A Maria J. Ledesma-Carbayo
%J ArXiv
%@ 2150-8145
%D 2020

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