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Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning

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Abstract

Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementations are tailored to two-dimensional (2D) datasets, limiting applicability to volumetric imaging (e.g., computed tomography). Targeting performance enhancement of a DNN algorithm based on a small image dataset, we assessed incremental impact of 3D-to-2D projection methods, one supporting novel data augmentation (DA); photometric grayscale-to-color conversion (GCC); and/or TL on training of an algorithm from a small coronary computed tomography angiography (CCTA) dataset (200 examinations, 50% with atherosclerosis and 50% atherosclerosis-free) producing 245 diseased and 1127 normal coronary arteries/branches. Volumetric CCTA data was converted to a 2D format creating both an Aggregate Projection View (APV) and a Mosaic Projection View (MPV), supporting DA per vessel; both grayscale and color-mapped versions of each view were also obtained. Training was performed both without and with TL, and algorithm performance of all permutations was compared using area under the receiver operating characteristics curve. Without TL, APV performance was 0.74 and 0.87 on grayscale and color images, respectively, compared to 0.90 and 0.87 for MPV. With TL, APV performance was 0.78 and 0.88 on grayscale and color images, respectively, compared with 0.93 and 0.91 for MPV. In conclusion, TL enhances performance of a DNN algorithm from a small volumetric dataset after proposed 3D-to-2D reformatting, but additive gain is achieved with application of either GCC to APV or the proposed novel MPV technique for DA.

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Funding

This project was partially funded by:

1. Donation from the Edward J. DeBartolo, Jr. Family.

2. Master Research Agreement with Siemens Healthineers.

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Authors and Affiliations

  1. Laboratory for Augmented Intelligence in Imaging-Division of Medical Imaging Informatics, Department of Radiology, Ohio State University College of Medicine, 395 West 12th Avenue, Columbus, OH, 43210, USA

    Vikash Gupta, Mutlu Demirer, Matthew Bigelow, Kevin J. Little, Sema Candemir, Luciano M. Prevedello, Richard D. White & Barbaros S. Erdal

  2. Siemens Healthineers, Malvern, PA, USA

    Thomas P. O’Donnell

  3. Siemens Healthineers, Erlangen, Germany

    Michael Wels

Authors
  1. Vikash Gupta
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  2. Mutlu Demirer
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  3. Matthew Bigelow
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  4. Kevin J. Little
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  5. Sema Candemir
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  6. Luciano M. Prevedello
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  7. Richard D. White
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  8. Thomas P. O’Donnell
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  10. Barbaros S. Erdal
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Corresponding author

Correspondence to Barbaros S. Erdal.

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All CCTA image-dataset utilization was retrospective, performed locally under Institutional Review Board approval (including HIPAA compliance) with the waiver of patient consent.

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Gupta, V., Demirer, M., Bigelow, M. et al. Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning. J Digit Imaging 33, 431–438 (2020). https://doi.org/10.1007/s10278-019-00267-3

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  • DOI: https://doi.org/10.1007/s10278-019-00267-3

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