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

Depth-resolved attenuation coefficient estimation for skin cancer assessment with Optical Coherence Tomography

Optical Coherence Tomography (OCT) is nowadays being widely employed as a diagnostic tool for skin cancer. It can produce feedback on tissue morphology alterations produced by different pathologies. OCT images are mainly produced by differences in refractive index and attenuation coefficient, providing in-depth information. Intensity OCT images display the effect of tissue alterations on backscattered light, but it does not represent real physical magnitude. In a number of occasions, morphology alteration events within the same tissue type, produce intensity variations in OCT images that can be misclassified as different tissue component. The estimation of depth-resolved attenuation coefficient improves tissue contrast, helping to identify tissue identity and isolating the effect of disordered structures of the same tissue. The proposed methodology shows that melanoma and Basal Cell Carcinoma (BCC) pathologies exhibit different optical parameters in depth. This enhances the identification of subsurface skin features.This research was funded FIS2010-19860 (DA2TOI), TEC2016-76021-C2-2-R (SENSA), DTS17-00055 (FUSIODERM), INNVAL 16/02 (DICUTEN), INNVAL 18/23 (DAPATOO) and co-funded with FEDER funds. This work has been developed thanks to the collaboration of the members of Dermatology Service at Marqués de Valdecilla University Hospital

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

On-line role-play as a teaching method in engineering studies

In this paper we propose adapting role-play teaching methodology to engineering studies. The role of a maintenance technician, a relevant job profile for engineering graduates is has chosen. The interaction is based on email exchange, with the instructor included in the simulation to help guide the activity and achieve learning objectives. In this paper, we present experience with this methodology, its implementation, results, and student feedback

Efficient processing technique based on plasma optical spectroscopy for on-line welding quality monitoring

In this paper a new spectroscopic analysis technique is proposed for on-line welding quality monitoring. This approach is based on the estimation of the wavelength associated with the maximum intensity of the background signal (continuum) of the welding plasma spectra. It will be demonstrated that this parameter exhibits a clear correlation with the welding quality of the seams, as it also happens with the traditional spectroscopic approach based on the determination of the plasma electronic temperature, thus allowing an identification of the appearance of weld defects. This technique offers a relevant improvement in terms of computational performance, what enables to detect smaller defects within the seam

In-process automatic wavelength calibration for CCD-spectrometers

In CCD-spectrometers, the relation between the CCD-pixel number and the associated wavelength is established by means of a calibration polynomial, whose coefficients are typically obtained using a calibration lamp with known emission line wavelengths and a regression procedure. A recalculation of this polynomial has to be performed periodically, as the pixel number versus wavelength relation can change with ambient temperature variations or modifications in the optics attached to the spectrometer connector. Given that this calibration procedure has to be performed off-line, it implies a disturbance for industrial scenarios, where the monitoring setup must be altered. In this paper an automatic wavelength calibration procedure for CCD-spectrometers is proposed. It is based on a processing scheme designed for the in-process estimation of the plasma electronic temperature, where several plasma emission lines are identified for each spectral capture. This identification stage involves the determination, by means of a sub-pixel algorithm, of the central wavelength of those lines, thus allowing an on-line wavelength calibration for each single acquired spectrum. The proposed technique will be demonstrated by means of several experimental arc-welding tests

Hyperspectral imaging sustains production-process competitiveness

A newly developed imaging system aids companies in the agri-food and industrial sectors to achieve high-speed online inspection and enhanced quality control

Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors

Optical spectroscopy has been utilized in various fields of science, industry and medicine, since each substance is discernible from all others by its spectral properties. However, optical spectroscopy traditionally generates information on the bulk properties of the whole sample, and mainly in the agri-food industry some product properties result from the heterogeneity in its composition. This monitoring is considerably more challenging and can be successfully achieved by the so-called hyperspectral imaging technology, which allows the simultaneous determination of the optical spectrum and the spatial location of an object in a surface. In addition, it is a nonintrusive and non-contact technique which gives rise to a great potential for industrial applications and it does not require any particular preparation of the samples, which is a primary concern in food monitoring. This work illustrates an overview of approaches based on this technology to address different problems in agri-food and industrial sectors. The hyperspectral system was originally designed and tested for raw material on-line discrimination, which is a key factor in the input stages of many industrial sectors. The combination of the acquisition of the spectral information across transversal lines while materials are being transported on a conveyor belt, and appropriate image analyses have been successfully validated in the tobacco industry. Lastly, the use of imaging spectroscopy applied to online welding quality monitoring is discussed and compared with traditional spectroscopic approaches in this regard

Use of the plasma RMS signal for on-line welding quality monitoring

In this paper a new spectroscopic monitoring parameter is proposed for the on-line monitoring of welding processes, the plasma RMS signal, which is determined by considering the contribution from the spectral samples over a particular spectral window. This parameter is directly related to the heat input that can be estimated by measuring both welding voltage and current, but it exhibits a higher sensitivity to the appearance of weld defects. A comparison between the results obtained from the different spectroscopic parameters will be presented, with data from both experimental and field arc-welding tests