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Med Phys. 2019 Oct 29. doi: 10.1002/mp.13880. [Epub ahead of print]

Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation.

Author information

1
Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, 20892, USA.
2
Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, 20892, USA.
3
Department of Computer Science, Middle Tennessee State University, Murfreesboro, TN, 37132, USA.
4
Department of Electrical Engineering, Vanderbilt University, Nashville, TN, 37235, USA.
5
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA.
6
Critical Illness, Brain Dysfunction & Survivorship (CIBS) Center, Section of Surgical Sciences, Department of Surgery, Division of Trauma, Emergency General Surgery, & Surgical Critical Care, Nashville, TN, 37212, USA.
7
Department of Computer Science, Vanderbilt University, Nashville, TN, 37235, USA.

Abstract

PURPOSE:

As deep neural networks achieve more success in the wide field of computer vision, greater emphasis is being placed on the generalizations of these models for production deployment. With sufficiently large training datasets, models can typically avoid overfitting their data; however, for medical imaging it is often difficult to obtain enough data from a single site. Sharing data between institutions is also frequently nonviable or prohibited due to security measures and research compliance constraints, enforced to guard protected health information (PHI) and patient anonymity.

METHODS:

In this paper, we implement cyclic weight transfer with independent datasets from multiple geographically disparate sites without compromising PHI. We compare results between single-site learning (SSL) and multisite learning (MSL) models on testing data drawn from each of the training sites as well as two other institutions.

RESULTS:

The MSL model attains an average dice similarity coefficient (DSC) of 0.690 on the holdout institution datasets with a volume correlation of 0.914, respectively corresponding to a 7% and 5% statistically significant improvement over the average of both SSL models, which attained an average DSC of 0.646 and average correlation of 0.871.

CONCLUSIONS:

We show that a neural network can be efficiently trained on data from two physically remote sites without consolidating patient data to a single location. The resulting network improves model generalization and achieves higher average DSCs on external datasets than neural networks trained on data from a single source.

KEYWORDS:

computed tomography (CT); deep learning; distributed; hemorrhage; image segmentation; lesion; multisite; neural network; traumatic brain injury

PMID:
31660621
DOI:
10.1002/mp.13880

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