Estimation of the number of fluorescent end-members for quantitative analysis of multispectral FLIM data.

Multispectral fluorescence lifetime imaging (m-FLIM) can potentially allow identifying the endogenous fluorophores present in biological tissue. Quantitative description of such data requires estimating the number of components in the sample, their characteristic fluorescent decays, and their relative contributions or abundances. Unfortunately, this inverse problem usually requires prior knowledge about the data, which is seldom available in biomedical applications. This work presents a new methodology to estimate the number of potential endogenous fluorophores present in biological tissue samples from time-domain m-FLIM data. Furthermore, a completely blind linear unmixing algorithm is proposed. The method was validated using both synthetic and experimental m-FLIM data. The experimental m-FLIM data include in-vivo measurements from healthy and cancerous hamster cheek-pouch epithelial tissue, and ex-vivo measurements from human coronary atherosclerotic plaques. The analysis of m-FLIM data from in-vivo hamster oral mucosa identified healthy from precancerous lesions, based on the relative concentration of their characteristic fluorophores. The algorithm also provided a better description of atherosclerotic plaques in term of their endogenous fluorophores. These results demonstrate the potential of this methodology to provide quantitative description of tissue biochemical composition.

Assessment of Singularities in the EEG During A-Phases of Sleep Based on Wavelet Decomposition.

Electroencephalography (EEG) signals convey information related to different processes that take place in the brain. From the EEG fluctuations during sleep, it is possible to establish the sleep stages and identify short events, commonly related to a specific physiological process or pathology. Some of these short events (called A-phases) present an organization and build up the concept of the Cyclic Alternating Pattern (CAP) phenomenon. In general, the A-phases abruptly modify the EEG fluctuations, and a singular behavior could occur. With the aim to quantify the abrupt changes during A-phases, in this work the wavelet analysis is considered to compute Hölder exponents, which measure the singularity strength. We considered time windows of 2s outside and 5s inside A-phases onset (or offset). A total number of 5121 A-phases from 9 healthy participants and 10 patients with periodic leg movements were analyzed. Within an A-phase the Hölder numerical value tends to be 0.6, which implies a less abrupt singularity. Whereas outside of A-phases, it is observed that the Hölder value is approximately equal to 0.3, which implies stronger singularities, i.e., a more evident discontinuity in the signal behavior. In addition, it seems that the number of singularities increases inside of A-phases. The numerical results suggest that the EEG naturally conveys singularities modified by the A-phase occurrence, and this information could help to conceptualize the CAP phenomenon from a new perspective based on the sharpness of the EEG instead of the oscillatory way.

COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using a U-NET and Probabilistic Active Contour Segmentation.

A two-step method for obtaining a volumetric estimation of COVID-19 related lesion from CT images is proposed. The first step consists in applying a U-NET convolutional neural network to provide a segmentation of the lung-parenchyma. This architecture is trained and validated using the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) dataset, which is publicly available. The second step consists in obtaining the volumetric lesion estimation using an automatic algorithm based on a probabilistic active contour (PACO) region delimitation approach. Our pipeline successfully segmented COVID-19 related lesions in CT images, with exception of some mislabeled regions including lung airways and vasculature. Our workflow was applied to images in a cohort of 50 patients.

A-phase classification using convolutional neural networks.

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. Graphical abstract A/N Deep Learning Classifier.

A new Probabilistic Active Contour region-based method for multiclass medical image segmentation.

In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed. Graphical Abstract.

Probabilistic Multiple Sclerosis Lesion Detection using Superpixels and Markov Random Fields / Detección Probabilística de Lesiones de Esclerosis Múltiple usando Superpixeles y Campos Aleatorios de Markov

Abstract Multiple Sclerosis (MS) is the most common neurodegenerative disease among young adults. Diagnosis and monitoring of MS is performed with T2-weighted or T2 FLAIR magnetic resonance imaging, where MS lesions appear as hyperintense spots in the white matter. In recent years, multiple algorithms have been proposed to detect these lesions with varying success rates, which greatly depend on the amount of a priori information required by each algorithm, such as the use of an atlas or the involvement of an expert to guide the segmentation process. In this work, a fully automatic method that does not rely on a priori anatomical information is proposed and evaluated. The proposed algorithm is based on an over-segmentation in superpixels and their classification by means of Gauss-Markov Measure Fields (GMMF). The main advantage of the over-segmentation is that it preserves the borders between tissues, while the GMMF classifier is robust to noise and computationally efficient. The proposed segmentation is then applied in two stages: first to segment the brain region and then to detect hyperintense spots within the brain. The proposed method is evaluated with synthetic images from BrainWeb, as well as real images from MS patients. The proposed method produces competitive results with respect to other algorithms in the state of the art, without requiring user assistance nor anatomical prior information.

Resumen La Esclerosis Múltiple (MS) es una de las enfermedades neurodegenerativas más comunes en adultos jóvenes. El diagnóstico y su monitoreo se realiza generalmente mediante imágenes de resonancia magnética T2 o T2 FLAIR, donde se observan regiones hiperintensas relacionadas a lesiones cerebrales causadas por la MS. En años recientes, múltiples algoritmos han sido propuestos para detectar estas lesiones con diferentes tasas de éxito las cuales dependen en gran medida de la cantidad de información a priori que requiere cada algoritmo, como el uso de un atlas o el involucramiento de un experto que guíe el proceso de segmentación. En este trabajo, se propone un método automático independiente de información anatómica. El algoritmo propuesto está basado en una sobresegmentación en superpixeles y su clasificación mediante un proceso de Campos Aleatorios de Markov de Medidas Gaussianas (GMMF). La principal ventaja de la sobresegmentación es que preserva bordes entre tejidos, además que tiene un costo reducido en tiempo de ejecución, mientras que el clasificador GMMF es robusto a ruido y computacionalmente eficiente. La segmentación propuesta es aplicada en dos etapas: primero para segmentar el cerebro y después para detectar las lesiones en él. El método propuesto es evaluado usando imágenes sintéticas de BrainWeb, así como también imágenes reales de pacientes con MS. Con respecto a los resultados, el método propuesto muestra un desempeño competitivo respecto a otros métodos en el estado del arte, tomando en cuenta que éste no requiere de asistencia o información a priori.

Blind deconvolution estimation of fluorescence measurements through quadratic programming.

Time-deconvolution of the instrument response from fluorescence lifetime imaging microscopy (FLIM) data is usually necessary for accurate fluorescence lifetime estimation. In many applications, however, the instrument response is not available. In such cases, a blind deconvolution approach is required. An iterative methodology is proposed to address the blind deconvolution problem departing from a dataset of FLIM measurements. A linear combination of a base conformed by Laguerre functions models the fluorescence impulse response of the sample at each spatial point in our formulation. Our blind deconvolution estimation (BDE) algorithm is formulated as a quadratic approximation problem, where the decision variables are the samples of the instrument response and the scaling coefficients of the basis functions. In the approximation cost function, there is a bilinear dependence on the decision variables. Hence, due to the nonlinear nature of the estimation process, an alternating least-squares scheme iteratively solves the approximation problem. Our proposal searches for the samples of the instrument response with a global perspective, and the scaling coefficients of the basis functions locally at each spatial point. First, the iterative methodology relies on a least-squares solution for the instrument response, and quadratic programming for the scaling coefficients applied just to a subset of the measured fluorescence decays to initially estimate the instrument response to speed up the convergence. After convergence, the final stage computes the fluorescence impulse response at all spatial points. A comprehensive validation stage considers synthetic and experimental FLIM datasets of ex vivo atherosclerotic plaques and human breast cancer cell samples that highlight the advantages of the proposed BDE algorithm under different noise and initial conditions in the iterative scheme and parameters of the proposal.

Blind end-member and abundance extraction for multispectral fluorescence lifetime imaging microscopy data.

This paper proposes a new blind end-member and abundance extraction (BEAE) method for multispectral fluorescence lifetime imaging microscopy (m-FLIM) data. The chemometrical analysis relies on an iterative estimation of the fluorescence decay end-members and their abundances. The proposed method is based on a linear mixture model with positivity and sum-to-one restrictions on the abundances and end-members to compensate for signature variability. The synthesis procedure depends on a quadratic optimization problem, which is solved by an alternating least-squares structure over convex sets. The BEAE strategy only assumes that the number of components in the analyzed sample is known a spriori. The proposed method is first validated by using synthetic m-FLIM datasets at 15, 20, and 25 dB signal-to-noise ratios. The samples simulate the mixed response of tissue containing multiple fluorescent intensity decays. Furthermore, the results were also validated with six m-FLIM datasets from fresh postmortem human coronary atherosclerotic plaques. A quantitative evaluation of the BEAE was made against two popular techniques: minimum volume constrained nonnegative matrix factorization (MVC-NMF) and multivariate curve resolution-alternating least-squares (MCR-ALS). Our proposed method (BEAE) was able to provide more accurate estimations of the end-members: 0.32% minimum relative error and 13.82% worst-case scenario, despite different initial conditions in the iterative optimization procedure and noise effect. Meanwhile, MVC-NMF and MCR-ALS presented more variability in estimating the end-members: 0.35% and 0.34% for minimum errors and 15.31% and 13.25% in the worst-case scenarios, respectively. This tendency was also maintained for the abundances, where BEAE obtained 0.05 as the minimum absolute error and 0.12 in the worst-case scenario; MCR-ALS and MVC-NMF achieved 0.04 and 0.06 for the minimum absolute errors, and 0.15 and 0.17 under the worst-case conditions, respectively. In addition, the average computation time was evaluated for the synthetic datasets, where MVC-NMF achieved the fastest time, followed by BEAE and finally MCR-ALS. Consequently, BEAE improved MVC-NMF in convergence to a local optimal solution and robustness against signal variability, and it is roughly 3.6 time faster than MCR-ALS.

Approximate string matching using phase correlation.

A novel method for approximate string matching with applications to bioinformatics is presented in this paper. Unlike most methods in the literature, the proposed method does not depend on the computation of the edit distance between two sequences, but uses instead a similarity index obtained by applying the phase correlation method. The resulting algorithm provides a finer control over the false positive rate, allowing users to pick out relevant matchings in less time, and can be applied for both offline and online processing.

Mesh-based approach for the 3D analysis of anatomical structures of interest in radiotherapy.

In this paper a method based on mesh surfaces approximations for the 3D analysis of anatomical structures in Radiotherapy (RT) is presented. Parotid glands meshes constructed from Megavoltage CT (MVCT) images were studied in terms of volume, distance between center of mass (distCOM) of the right and left parotids, dice similarity coefficient (DICE), maximum distance between meshes (DMax) and the average symmetric distance (ASD). A comparison with the standard binary images approach was performed. While absence of significant differences in terms of volume, DistCOM and DICE indices suggests that both approaches are comparable, the fact that the ASD showed significant difference (p=0.002) and the DMax was almost significant (p=0.053) suggests that the mesh approach should be adopted to provide accurate comparison between 3D anatomical structures of interest in RT.