Cargando…
Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis
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 cro...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537099/ https://www.ncbi.nlm.nih.gov/pubmed/28653123 http://dx.doi.org/10.1007/s10278-017-9988-z |
_version_ | 1783254106868023296 |
---|---|
author | Lee, Hyunkwang Troschel, Fabian M. Tajmir, Shahein Fuchs, Georg Mario, Julia Fintelmann, Florian J. Do, Synho |
author_facet | Lee, Hyunkwang Troschel, Fabian M. Tajmir, Shahein Fuchs, Georg Mario, Julia Fintelmann, Florian J. Do, Synho |
author_sort | Lee, Hyunkwang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5537099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-55370992017-08-15 Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis Lee, Hyunkwang Troschel, Fabian M. Tajmir, Shahein Fuchs, Georg Mario, Julia Fintelmann, Florian J. Do, Synho J Digit Imaging Article 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. Springer International Publishing 2017-06-26 2017-08 /pmc/articles/PMC5537099/ /pubmed/28653123 http://dx.doi.org/10.1007/s10278-017-9988-z Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Lee, Hyunkwang Troschel, Fabian M. Tajmir, Shahein Fuchs, Georg Mario, Julia Fintelmann, Florian J. Do, Synho Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis |
title | Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis |
title_full | Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis |
title_fullStr | Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis |
title_full_unstemmed | Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis |
title_short | Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis |
title_sort | pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537099/ https://www.ncbi.nlm.nih.gov/pubmed/28653123 http://dx.doi.org/10.1007/s10278-017-9988-z |
work_keys_str_mv | AT leehyunkwang pixelleveldeepsegmentationartificialintelligencequantifiesmuscleoncomputedtomographyforbodymorphometricanalysis AT troschelfabianm pixelleveldeepsegmentationartificialintelligencequantifiesmuscleoncomputedtomographyforbodymorphometricanalysis AT tajmirshahein pixelleveldeepsegmentationartificialintelligencequantifiesmuscleoncomputedtomographyforbodymorphometricanalysis AT fuchsgeorg pixelleveldeepsegmentationartificialintelligencequantifiesmuscleoncomputedtomographyforbodymorphometricanalysis AT mariojulia pixelleveldeepsegmentationartificialintelligencequantifiesmuscleoncomputedtomographyforbodymorphometricanalysis AT fintelmannflorianj pixelleveldeepsegmentationartificialintelligencequantifiesmuscleoncomputedtomographyforbodymorphometricanalysis AT dosynho pixelleveldeepsegmentationartificialintelligencequantifiesmuscleoncomputedtomographyforbodymorphometricanalysis |