Modeling and synthesis of breast cancer optical property signatures with generative models
Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range...
| Autores: | , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/21833 |
| Acceso en línea: | http://hdl.handle.net/10902/21833 |
| Access Level: | acceso abierto |
| Palabra clave: | Biomedical optical imaging Breast cancer Tissue optical properties Modeling Pathology Deep learning Dimensionality reduction Variational autoencoder Convolutional neural networks |
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Modeling and synthesis of breast cancer optical property signatures with generative modelsPardo Franco, Arturo|||0000-0003-3362-3485Streeter, Samuel S.Maloney, Benjamin W.Gutiérrez Gutiérrez, José Alberto|||0000-0002-9822-9406McClatchy, David M.Wells, Wendy A.Paulsen, Keith D.López Higuera, José Miguel|||0000-0002-8615-8487Pogue, Brian WilliamConde Portilla, Olga María|||0000-0002-2471-3051Biomedical optical imagingBreast cancerTissue optical propertiesModelingPathologyDeep learningDimensionality reductionVariational autoencoderConvolutional neural networksIs it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.This work was supported in part by the National Cancer Institute, US National Institutes of Health, under grants R01 CA192803 and F31 CA196308, by the Spanish Ministry of Science and Innovation under grant FIS2010-19860, by the Spanish Ministry of Science, Innovation and Universities under grants TEC2016-76021-C2-2-R and PID2019-107270RB-C21, by the Spanish Minstry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III via DTS17-00055, by IDIVAL under grants INNVAL 16/02, and INNVAL 18/23, and by the Spanish Ministry of Education, Culture, and Sports with PhD grant FPU16/05705, as well as FEDER funds.Institute of Electrical and Electronics Engineers Inc.Universidad de Cantabria20212021-06-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/21833IEEE Transactions on Medical Imaging, 2021, 40(6), 1687-1701reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/218332026-06-02T12:39:31Z |
| dc.title.none.fl_str_mv |
Modeling and synthesis of breast cancer optical property signatures with generative models |
| title |
Modeling and synthesis of breast cancer optical property signatures with generative models |
| spellingShingle |
Modeling and synthesis of breast cancer optical property signatures with generative models Pardo Franco, Arturo|||0000-0003-3362-3485 Biomedical optical imaging Breast cancer Tissue optical properties Modeling Pathology Deep learning Dimensionality reduction Variational autoencoder Convolutional neural networks |
| title_short |
Modeling and synthesis of breast cancer optical property signatures with generative models |
| title_full |
Modeling and synthesis of breast cancer optical property signatures with generative models |
| title_fullStr |
Modeling and synthesis of breast cancer optical property signatures with generative models |
| title_full_unstemmed |
Modeling and synthesis of breast cancer optical property signatures with generative models |
| title_sort |
Modeling and synthesis of breast cancer optical property signatures with generative models |
| dc.creator.none.fl_str_mv |
Pardo Franco, Arturo|||0000-0003-3362-3485 Streeter, Samuel S. Maloney, Benjamin W. Gutiérrez Gutiérrez, José Alberto|||0000-0002-9822-9406 McClatchy, David M. Wells, Wendy A. Paulsen, Keith D. López Higuera, José Miguel|||0000-0002-8615-8487 Pogue, Brian William Conde Portilla, Olga María|||0000-0002-2471-3051 |
| author |
Pardo Franco, Arturo|||0000-0003-3362-3485 |
| author_facet |
Pardo Franco, Arturo|||0000-0003-3362-3485 Streeter, Samuel S. Maloney, Benjamin W. Gutiérrez Gutiérrez, José Alberto|||0000-0002-9822-9406 McClatchy, David M. Wells, Wendy A. Paulsen, Keith D. López Higuera, José Miguel|||0000-0002-8615-8487 Pogue, Brian William Conde Portilla, Olga María|||0000-0002-2471-3051 |
| author_role |
author |
| author2 |
Streeter, Samuel S. Maloney, Benjamin W. Gutiérrez Gutiérrez, José Alberto|||0000-0002-9822-9406 McClatchy, David M. Wells, Wendy A. Paulsen, Keith D. López Higuera, José Miguel|||0000-0002-8615-8487 Pogue, Brian William Conde Portilla, Olga María|||0000-0002-2471-3051 |
| author2_role |
author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Universidad de Cantabria |
| dc.subject.none.fl_str_mv |
Biomedical optical imaging Breast cancer Tissue optical properties Modeling Pathology Deep learning Dimensionality reduction Variational autoencoder Convolutional neural networks |
| topic |
Biomedical optical imaging Breast cancer Tissue optical properties Modeling Pathology Deep learning Dimensionality reduction Variational autoencoder Convolutional neural networks |
| description |
Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021 2021-06-01 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 NA http://purl.org/coar/version/c_be7fb7dd8ff6fe43 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10902/21833 |
| url |
http://hdl.handle.net/10902/21833 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers Inc. |
| publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers Inc. |
| dc.source.none.fl_str_mv |
IEEE Transactions on Medical Imaging, 2021, 40(6), 1687-1701 reponame:UCrea Repositorio Abierto de la Universidad de Cantabria instname:Universidad de Cantabria (UC) |
| instname_str |
Universidad de Cantabria (UC) |
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UCrea Repositorio Abierto de la Universidad de Cantabria |
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UCrea Repositorio Abierto de la Universidad de Cantabria |
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15,301603 |