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DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation
Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different e...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776609/ https://www.ncbi.nlm.nih.gov/pubmed/36553079 http://dx.doi.org/10.3390/diagnostics12123072 |
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author | Chamroukhi, Faicel Brivet, Segolene Savadjiev, Peter Coates, Mark Forghani, Reza |
author_facet | Chamroukhi, Faicel Brivet, Segolene Savadjiev, Peter Coates, Mark Forghani, Reza |
author_sort | Chamroukhi, Faicel |
collection | PubMed |
description | Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different effective scanning energy levels. In this paper, we develop novel unsupervised learning techniques based on mixture models and functional data analysis models to the clustering of DECT images. We design functional mixture models that integrate spatial image context in mixture weights, with mixture component densities being constructed upon the DECT energy decay curves as functional observations. We develop dedicated expectation–maximization algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to develop statistical functional data analysis and model-based clustering techniques to take advantage of the full spectral information provided by DECT. We evaluate the application of DECT to head and neck squamous cell carcinoma. Current image-based evaluation of these tumors in clinical practice is largely qualitative, based on a visual assessment of tumor anatomic extent and basic one- or two-dimensional tumor size measurements. We evaluate our methods on 91 head and neck cancer DECT scans and compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer. |
format | Online Article Text |
id | pubmed-9776609 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97766092022-12-23 DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation Chamroukhi, Faicel Brivet, Segolene Savadjiev, Peter Coates, Mark Forghani, Reza Diagnostics (Basel) Article Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different effective scanning energy levels. In this paper, we develop novel unsupervised learning techniques based on mixture models and functional data analysis models to the clustering of DECT images. We design functional mixture models that integrate spatial image context in mixture weights, with mixture component densities being constructed upon the DECT energy decay curves as functional observations. We develop dedicated expectation–maximization algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to develop statistical functional data analysis and model-based clustering techniques to take advantage of the full spectral information provided by DECT. We evaluate the application of DECT to head and neck squamous cell carcinoma. Current image-based evaluation of these tumors in clinical practice is largely qualitative, based on a visual assessment of tumor anatomic extent and basic one- or two-dimensional tumor size measurements. We evaluate our methods on 91 head and neck cancer DECT scans and compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer. MDPI 2022-12-06 /pmc/articles/PMC9776609/ /pubmed/36553079 http://dx.doi.org/10.3390/diagnostics12123072 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chamroukhi, Faicel Brivet, Segolene Savadjiev, Peter Coates, Mark Forghani, Reza DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation |
title | DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation |
title_full | DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation |
title_fullStr | DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation |
title_full_unstemmed | DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation |
title_short | DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation |
title_sort | dect-clust: dual-energy ct image clustering and application to head and neck squamous cell carcinoma segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776609/ https://www.ncbi.nlm.nih.gov/pubmed/36553079 http://dx.doi.org/10.3390/diagnostics12123072 |
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