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3D digital breast cancer models with multimodal fusion algorithms

Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that inte...

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Autores principales: Bessa, Sílvia, Gouveia, Pedro F., Carvalho, Pedro H., Rodrigues, Cátia, Silva, Nuno L., Cardoso, Fátima, Cardoso, Jaime S., Oliveira, Hélder P., Cardoso, Maria João
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375583/
https://www.ncbi.nlm.nih.gov/pubmed/31986378
http://dx.doi.org/10.1016/j.breast.2019.12.016
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author Bessa, Sílvia
Gouveia, Pedro F.
Carvalho, Pedro H.
Rodrigues, Cátia
Silva, Nuno L.
Cardoso, Fátima
Cardoso, Jaime S.
Oliveira, Hélder P.
Cardoso, Maria João
author_facet Bessa, Sílvia
Gouveia, Pedro F.
Carvalho, Pedro H.
Rodrigues, Cátia
Silva, Nuno L.
Cardoso, Fátima
Cardoso, Jaime S.
Oliveira, Hélder P.
Cardoso, Maria João
author_sort Bessa, Sílvia
collection PubMed
description Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient’s breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice.
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spelling pubmed-73755832020-07-29 3D digital breast cancer models with multimodal fusion algorithms Bessa, Sílvia Gouveia, Pedro F. Carvalho, Pedro H. Rodrigues, Cátia Silva, Nuno L. Cardoso, Fátima Cardoso, Jaime S. Oliveira, Hélder P. Cardoso, Maria João Breast Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient’s breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice. Elsevier 2020-01-03 /pmc/articles/PMC7375583/ /pubmed/31986378 http://dx.doi.org/10.1016/j.breast.2019.12.016 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi
Bessa, Sílvia
Gouveia, Pedro F.
Carvalho, Pedro H.
Rodrigues, Cátia
Silva, Nuno L.
Cardoso, Fátima
Cardoso, Jaime S.
Oliveira, Hélder P.
Cardoso, Maria João
3D digital breast cancer models with multimodal fusion algorithms
title 3D digital breast cancer models with multimodal fusion algorithms
title_full 3D digital breast cancer models with multimodal fusion algorithms
title_fullStr 3D digital breast cancer models with multimodal fusion algorithms
title_full_unstemmed 3D digital breast cancer models with multimodal fusion algorithms
title_short 3D digital breast cancer models with multimodal fusion algorithms
title_sort 3d digital breast cancer models with multimodal fusion algorithms
topic Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375583/
https://www.ncbi.nlm.nih.gov/pubmed/31986378
http://dx.doi.org/10.1016/j.breast.2019.12.016
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