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Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging
BACKGROUND: Since three-dimensional segmentation of cardiac region in (123)I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with (123)I-MIBG single photon emission computed tomography (SPECT) im...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8511236/ https://www.ncbi.nlm.nih.gov/pubmed/34637028 http://dx.doi.org/10.1186/s13550-021-00847-x |
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author | Saito, Shintaro Nakajima, Kenichi Edenbrandt, Lars Enqvist, Olof Ulén, Johannes Kinuya, Seigo |
author_facet | Saito, Shintaro Nakajima, Kenichi Edenbrandt, Lars Enqvist, Olof Ulén, Johannes Kinuya, Seigo |
author_sort | Saito, Shintaro |
collection | PubMed |
description | BACKGROUND: Since three-dimensional segmentation of cardiac region in (123)I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with (123)I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging. METHODS: We assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late (123)I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of (123)I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them. RESULTS: The CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R(2) = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R(2) = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods. CONCLUSIONS: The CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-021-00847-x. |
format | Online Article Text |
id | pubmed-8511236 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85112362021-10-27 Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging Saito, Shintaro Nakajima, Kenichi Edenbrandt, Lars Enqvist, Olof Ulén, Johannes Kinuya, Seigo EJNMMI Res Original Research BACKGROUND: Since three-dimensional segmentation of cardiac region in (123)I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with (123)I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging. METHODS: We assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late (123)I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of (123)I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them. RESULTS: The CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R(2) = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R(2) = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods. CONCLUSIONS: The CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-021-00847-x. Springer Berlin Heidelberg 2021-10-12 /pmc/articles/PMC8511236/ /pubmed/34637028 http://dx.doi.org/10.1186/s13550-021-00847-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Saito, Shintaro Nakajima, Kenichi Edenbrandt, Lars Enqvist, Olof Ulén, Johannes Kinuya, Seigo Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging |
title | Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging |
title_full | Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging |
title_fullStr | Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging |
title_full_unstemmed | Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging |
title_short | Convolutional neural network-based automatic heart segmentation and quantitation in (123)I-metaiodobenzylguanidine SPECT imaging |
title_sort | convolutional neural network-based automatic heart segmentation and quantitation in (123)i-metaiodobenzylguanidine spect imaging |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8511236/ https://www.ncbi.nlm.nih.gov/pubmed/34637028 http://dx.doi.org/10.1186/s13550-021-00847-x |
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