Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury release_6zai3pb4efhptccucyq65x5anm

by Samuel Remedios, Snehashis Roy, Justin Blaber, Camilo Bermudez, Vishwesh Nath, Mayur B. Patel, John A. Butman, Bennett A. Landman, Dzung L. Pham

Released as a article .

2019  

Abstract

Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient anonymity it is frequently impractical or prohibited to transfer protected health information (PHI) between institutions. Additionally, due to the nature of some studies, there may not be a large public dataset available on which to train models. To address this conundrum, we analyze the efficacy of transferring the model itself in lieu of data between different sites. By doing so we accomplish two goals: 1) the model gains access to training on a larger dataset that it could not normally obtain and 2) the model better generalizes, having trained on data from separate locations. In this paper, we implement multi-site learning with disparate datasets from the National Institutes of Health (NIH) and Vanderbilt University Medical Center (VUMC) without compromising PHI. Three neural networks are trained to convergence on a computed tomography (CT) brain hematoma segmentation task: one only with NIH data,one only with VUMC data, and one multi-site model alternating between NIH and VUMC data. Resultant lesion masks with the multi-site model attain an average Dice similarity coefficient of 0.64 and the automatically segmented hematoma volumes correlate to those done manually with a Pearson correlation coefficient of 0.87,corresponding to an 8% and 5% improvement, respectively, over the single-site model counterparts.
In text/plain format

Archived Files and Locations

application/pdf  2.5 MB
file_wdib6ookh5cgpjxkuadki4uddu
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2019-03-11
Version   v1
Language   en ?
arXiv  1903.04207v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: e7ca492a-789d-48ee-aee9-92ebb22ae1b5
API URL: JSON