Academic Journal
Modeling and synthesis of breast cancer optical property signatures with generative models
| العنوان: | Modeling and synthesis of breast cancer optical property signatures with generative models |
|---|---|
| المؤلفون: | Pardo Franco, Arturo, Streeter, Samuel S., Maloney, Benjamin W., Gutiérrez Gutiérrez, José Alberto, McClatchy, David M., Wells, Wendy A., Paulsen, Keith D., López Higuera, José Miguel, Pogue, Brian William, Conde Portilla, Olga María |
| المساهمون: | Universidad de Cantabria |
| المصدر: | IEEE Transactions on Medical Imaging, 2021, 40(6), 1687-1701 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers Inc. |
| سنة النشر: | 2021 |
| المجموعة: | Universidad de Cantabria: UCrea |
| مصطلحات موضوعية: | Biomedical optical imaging, Breast cancer, Tissue optical properties, Modeling, Pathology, Deep learning, Dimensionality reduction, Variational autoencoder, Convolutional neural networks |
| الوصف: | 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. ; 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 0278-0062 1558-254X |
| Relation: | https://doi.org/10.1109/TMI.2021.3064464; FIS2010-19860; TEC2016-76021-C2-2-R; PID2019-107270RB-C21; http://hdl.handle.net/10902/21833 |
| DOI: | 10.1109/TMI.2021.3064464 |
| الاتاحة: | http://hdl.handle.net/10902/21833 https://doi.org/10.1109/TMI.2021.3064464 |
| Rights: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; openAccess |
| رقم الانضمام: | edsbas.6877B3A5 |
| قاعدة البيانات: | BASE |
| تدمد: | 02780062 1558254X |
|---|---|
| DOI: | 10.1109/TMI.2021.3064464 |