Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI

Extracting pathology information embedded within surface optical properties in Spatial Frequency Domain Imaging (SFDI) datasets is still a rather cumbersome nonlinear translation problem, mainly constrained by intrasample and interpatient variability, as well as dataset size. The B-variational autoencoder (B-VAE) is a rather novel dimensionality reduction technique where a tractable set of latent low-dimensional embeddings can be obtained from a given dataset. These embeddings can then be sampled to synthesize new data, providing further insight into pathology variability as well as differentiability in terms of optical properties. Its applications for data classification and breast margin delineation are also discussed.Research reported in this manuscript was funded by PhD grant FPU016/05705 (Spanish Ministry of Education, Culture and Sports), projects DTS1700055 (FUSIODERM), INNVAL 16/02 (DICUTEN), INNVAL 18/23 (DAPATOO), and TEC201676021C22R (SENSA), as well as cofunded with FEDER funds

ROTDR signal enhancement via deep convolutional denoising autoencoders trained with domain randomization

In this work, a deep convolutional adaptive filter is proposed to enhance the performance of a Raman based distributed temperature sensor system by the application of domain randomization methods for its training. The improvement of the signal-to-noise ratio in the Raman backscattered signals in the training process and translation to a real scenario is demonstrated. The ability of the proposed technique to reduce signal noise effectively is proved independently of the sensor configuration and without degradation of temperature accuracy or spatial resolution of these systems. Moreover, using single trace to noise reduction in the ROTDR signals accelerates the system response avoiding the employment of many averages in a unique measurement.This work has been supported by Spanish CICYT (TEC2016-76021-C2-2-R), by ISCIII (DTS17-00055, INTRACARDIO) co-funded by EU-FEDER FUNDS and by the Spanish Ministry of Education, Culture and Sports through FPU16/05705

Fusion of OCT and hyperspectral imaging for tissue diagnosis and assessment

The combination of molecular (hyperspectral imaging) and morphological (optical coherent tomography imaging) optical technologies helps in the assessment of biological tissue both in pathological diagnosis and in the follow-up treatments. The co-registration of both imaging features allows quantifying the presence of chromophores and the subsurface structure of tissue. This work proposes the fusion of two optical imaging technologies for the characterization of different types of tissues where the attenuation coefficient calculated from OCT imaging serves to track the presence of anomalies in the distribution of chromophores over the sample and therefore to diagnose pathological conditions. The performance of two customized hyperspectral imaging systems working in two complementary spectral ranges (VisNIR from 400 to 1000 nm, and SWIR 1000 to 1700 nm) and one commercial OCT system working at 1325 nm reveals the presence of fibrosis, collagen alterations and lipid content in cardiovascular tissues such as aortic walls (to assess on aneurysmal conditions) or tendinous chords (to diagnose the integrity of the valvular system) or in muscular diseases prone to fibrotic changes and inflammation.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23) and Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Affinity-based color enhancement methods for contrast enhancement in hyperspectral and multimodal imaging

This work proposes separating data analysis from hyperspectral enhancement or editing, providing a robust, context-independent, fully-tunable framework for biomarker-based contrast in wide-field imaging with a series of reliable properties that could enable its use in guided surgery. Some applications of this method powered by deep learning diagnostics will be discussed and shown.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Directional kernel density estimation for classification of breast tissue spectra

In Breast Conserving Therapy, surgeons measure the thickness of healthy tissue surrounding an excised tumor (surgical margin) via post-operative histological or visual assessment tests that, for lack of enough standardization and reliability, have recurrence rates in the order of 33%. Spectroscopic interrogation of these margins is possible during surgery, but algorithms are needed for parametric or dimension reduction processing. One methodology for tumor discrimination based on dimensionality reduction and nonparametric estimation - in particular, Directional Kernel Density Estimation - is proposed and tested on spectral image data from breast samples. Once a hyperspectral image of the tumor has been captured, a surgeon assists by establishing Regions of Interest where tissues are qualitatively differentiable. After proper normalization, Directional KDE is used to estimate the likelihood of every pixel in the image belonging to each specified tissue class. This information is enough to yield, in almost real time and with 98% accuracy, results that coincide with those provided by histological H&E validation performed after the surgery.Research reported in this paper was funded by projects DA2TOI (codename FIS 2010-19860), FOS4 (codename TEC 2013-47264-C2-1-R) and an undergraduate Research Assistant Fellowship (Beca de Colaboración) entitled “Multispectralenhancement systems for tissue diagnosis in oncology and cardiovascularmedicine,” the latter granted to themain author by the SpanishMinistry of Education, Culture and Sports

Automated skin lesion segmentation with kernel density estimation

Skin lesion segmentation is a complex step for dermoscopy pathological diagnosis. Kernel density estimation is proposed as a segmentation technique based on the statistic distribution of color intensities in the lesion and non-lesion regions.This work is supported by the “Ministerio de Economía, Industria y Competitividad” (MINECO) under projects DA2TOI (FIS2010-19860), SENSA (TEC2016-76021-C2-2-R), the “Instituto de Salud Carlos III” (ISCIII) through projects FUSIODERM (DTS15/00238) and CIBERBBN and the co-financed by FEDER funds

Scatter signatures in SFDI data enable breast surgical margin delineation via ensemble learning

Margin assessment in gross pathology is becoming feasible as various explanatory deep learning-powered methods are able to obtain models for macroscopic textural information, tissue microstructure, and local surface optical properties. Unfortunately, each different method seems to lack enough diagnostic power to perform an adequate classification on its own. This work proposes using several separately trained deep convolutional networks, and averaging their responses, in order to achieve a better margin assessment. Qualitative leave-one-out cross-validation results are discussed for a cohort of 70 samples.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Automated surgical margin assessment in breast conserving surgery using SFDI with ensembles of self-confident deep convolutional networks

With an adequate tissue dataset, supervised classification of tissue optical properties can be achieved in SFDI images of breast cancer lumpectomies with deep convolutional networks. Nevertheless, the use of a black-box classifier in current ex vivo setups provides output diagnostic images that are inevitably bound to show misclassified areas due to inter- and intra-patient variability that could potentially be misinterpreted in a real clinical setting. This work proposes the use of a novel architecture, the self-introspective classifier, where part of the model is dedicated to estimating its own expected classification error. The model can be used to generate metrics of self-confidence for a given classification problem, which can then be employed to show how much the network is familiar with the new incoming data. A heterogenous ensemble of four deep convolutional models with self-confidence, each sensitive to a different spatial scale of features, is tested on a cohort of 70 specimens, achieving a global leave-one-out cross-validation accuracy of up to 81%, while being able to explain where in the output classification image the system is most confident.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Realce multiespectral de tejidos tumorales

En un procedimiento de resección quirúrgica de cáncer convencional siempre existe cierta problemática a la hora de delimitar el contorno del tumor. Se propone un método de reconstrucción de imagen multiespectral basado en la descomposición en valores singulares de la reflectancia local del tejido, su proyección sobre la N-esfera de radio unidad y una métrica inspirada en las superficies equipotenciales electrostáticas. Este método realza el brillo/color/contraste de los tejidos elegidos por el cirujano

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 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