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Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis
In breast tomosynthesis, multiple low-dose projections are acquired in a single scanning direction over a limited angular range to produce cross-sectional planes through the breast for three-dimensional imaging interpretation. We built a next-generation tomosynthesis system capable of multidirection...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303463/ https://www.ncbi.nlm.nih.gov/pubmed/37368544 http://dx.doi.org/10.3390/tomography9030092 |
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author | Barufaldi, Bruno da Nobrega, Yann N. G. Carvalhal, Giulia Teixeira, Joao P. V. Silva Filho, Telmo M. do Rego, Thais G. Malheiros, Yuri Acciavatti, Raymond J. Maidment, Andrew D. A. |
author_facet | Barufaldi, Bruno da Nobrega, Yann N. G. Carvalhal, Giulia Teixeira, Joao P. V. Silva Filho, Telmo M. do Rego, Thais G. Malheiros, Yuri Acciavatti, Raymond J. Maidment, Andrew D. A. |
author_sort | Barufaldi, Bruno |
collection | PubMed |
description | In breast tomosynthesis, multiple low-dose projections are acquired in a single scanning direction over a limited angular range to produce cross-sectional planes through the breast for three-dimensional imaging interpretation. We built a next-generation tomosynthesis system capable of multidirectional source motion with the intent to customize scanning motions around “suspicious findings”. Customized acquisitions can improve the image quality in areas that require increased scrutiny, such as breast cancers, architectural distortions, and dense clusters. In this paper, virtual clinical trial techniques were used to analyze whether a finding or area at high risk of masking cancers can be detected in a single low-dose projection and thus be used for motion planning. This represents a step towards customizing the subsequent low-dose projection acquisitions autonomously, guided by the first low-dose projection; we call this technique “self-steering tomosynthesis.” A U-Net was used to classify the low-dose projections into “risk classes” in simulated breasts with soft-tissue lesions; class probabilities were modified using post hoc Dirichlet calibration (DC). DC improved the multiclass segmentation (Dice = 0.43 vs. 0.28 before DC) and significantly reduced false positives (FPs) from the class of the highest risk of masking (sensitivity = 81.3% at 2 FPs per image vs. 76.0%). This simulation-based study demonstrated the feasibility of identifying suspicious areas using a single low-dose projection for self-steering tomosynthesis. |
format | Online Article Text |
id | pubmed-10303463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103034632023-06-29 Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis Barufaldi, Bruno da Nobrega, Yann N. G. Carvalhal, Giulia Teixeira, Joao P. V. Silva Filho, Telmo M. do Rego, Thais G. Malheiros, Yuri Acciavatti, Raymond J. Maidment, Andrew D. A. Tomography Article In breast tomosynthesis, multiple low-dose projections are acquired in a single scanning direction over a limited angular range to produce cross-sectional planes through the breast for three-dimensional imaging interpretation. We built a next-generation tomosynthesis system capable of multidirectional source motion with the intent to customize scanning motions around “suspicious findings”. Customized acquisitions can improve the image quality in areas that require increased scrutiny, such as breast cancers, architectural distortions, and dense clusters. In this paper, virtual clinical trial techniques were used to analyze whether a finding or area at high risk of masking cancers can be detected in a single low-dose projection and thus be used for motion planning. This represents a step towards customizing the subsequent low-dose projection acquisitions autonomously, guided by the first low-dose projection; we call this technique “self-steering tomosynthesis.” A U-Net was used to classify the low-dose projections into “risk classes” in simulated breasts with soft-tissue lesions; class probabilities were modified using post hoc Dirichlet calibration (DC). DC improved the multiclass segmentation (Dice = 0.43 vs. 0.28 before DC) and significantly reduced false positives (FPs) from the class of the highest risk of masking (sensitivity = 81.3% at 2 FPs per image vs. 76.0%). This simulation-based study demonstrated the feasibility of identifying suspicious areas using a single low-dose projection for self-steering tomosynthesis. MDPI 2023-06-10 /pmc/articles/PMC10303463/ /pubmed/37368544 http://dx.doi.org/10.3390/tomography9030092 Text en © 2023 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 Barufaldi, Bruno da Nobrega, Yann N. G. Carvalhal, Giulia Teixeira, Joao P. V. Silva Filho, Telmo M. do Rego, Thais G. Malheiros, Yuri Acciavatti, Raymond J. Maidment, Andrew D. A. Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis |
title | Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis |
title_full | Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis |
title_fullStr | Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis |
title_full_unstemmed | Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis |
title_short | Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis |
title_sort | multiclass segmentation of breast tissue and suspicious findings: a simulation-based study for the development of self-steering tomosynthesis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303463/ https://www.ncbi.nlm.nih.gov/pubmed/37368544 http://dx.doi.org/10.3390/tomography9030092 |
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