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Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 4/2017

Open Access 01-08-2017

Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis

Authors: Hyunkwang Lee, Fabian M. Troschel, Shahein Tajmir, Georg Fuchs, Julia Mario, Florian J. Fintelmann, Synho Do

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2017

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Abstract

Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an “eyeball test” to assess whether patients will tolerate major surgery or chemotherapy, “eyeballing” is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts. In this study, we propose a fully automated deep learning system for the segmentation of skeletal muscle cross-sectional area (CSA) on an axial computed tomography image taken at the third lumbar vertebra. We utilized a fully automated deep segmentation model derived from an extended implementation of a fully convolutional network with weight initialization of an ImageNet pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis. This experiment was conducted by varying window level (WL), window width (WW), and bit resolutions in order to better understand the effects of the parameters on the model performance. Our best model, fine-tuned on 250 training images and ground truth labels, achieves 0.93 ± 0.02 Dice similarity coefficient (DSC) and 3.68 ± 2.29% difference between predicted and ground truth muscle CSA on 150 held-out test cases. Ultimately, the fully automated segmentation system can be embedded into the clinical environment to accelerate the quantification of muscle and expanded to volume analysis of 3D datasets.
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Metadata
Title
Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis
Authors
Hyunkwang Lee
Fabian M. Troschel
Shahein Tajmir
Georg Fuchs
Julia Mario
Florian J. Fintelmann
Synho Do
Publication date
01-08-2017
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 4/2017
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-017-9988-z

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