Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks View Full Text


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Article Info

DATE

2021-03-25

AUTHORS

Kambiz Nael, Eli Gibson, Chen Yang, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Amish Doshi, Bogdan Georgescu, Nirmal Janardhanan, Benjamin Odry, Mariappan Nadar, Michael Bush, Thomas J. Re, Stefan Huwer, Sonal Josan, Heinrich von Busch, Heiko Meyer, David Mendelson, Burton P. Drayer, Dorin Comaniciu, Zahi A. Fayad

ABSTRACT

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies. More... »

PAGES

6876

References to SciGraph publications

  • 2015. Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers in ADVANCES IN INTELLIGENT INFORMATICS
  • 2017-11-14. Deep Learning for Medical Image Processing: Overview, Challenges and the Future in CLASSIFICATION IN BIOAPPS
  • 2018-09-26. Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations in MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION – MICCAI 2018
  • 2016-09-13. A novel training algorithm for convolutional neural network in COMPLEX & INTELLIGENT SYSTEMS
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  • 2018-04-04. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration in NPJ DIGITAL MEDICINE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-021-86022-7

    DOI

    http://dx.doi.org/10.1038/s41598-021-86022-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1136652750

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/33767226


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