Automatic image characterization of psoriasis lesions

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-76770-

Automatic image characterization of psoriasis lesions

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, par-ticularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions

Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation

The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods

Step-wise Integration of Deep Class-specific Learning for Dermoscopic Image Segmentation

The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our experiments on three well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation

Markov-Gibbs Random Field Approach for Modeling of Skin Surface Textures

Medical imaging has been contributing to dermatology by providing computer-based assistance by 2D digital imaging of skin and processing of images. Skin imaging can be more effective by inclusion of 3D skin features. Furthermore, clinical examination of skin consists of both visual and tactile inspection. The tactile sensation is related to 3D surface profiles and mechanical parameters. The 3D imaging of skin can also be integrated with haptic technology for computer-based tactile inspection. The research objective of this work is to model 3D surface textures of skin. A 3D image acquisition set up capturing skin surface textures at high resolution (~0.1 mm) has been used. An algorithm to extract 2D grayscale texture (height map) from 3D texture has been presented. The extracted 2D textures are then modeled using Markov-Gibbs random field (MGRF) modeling technique. The modeling results for MGRF model depend on input texture characteristics. The homogeneous, spatially invariant texture patterns are modeled successfully. From the observation of skin samples, we classify three key features of3D skin profiles i.e. curvature of underlying limb, wrinkles/line like features and fine textures. The skin samples are distributed in three input sets to see the MGRF model's response to each of these 3D features. First set consists of all three features. Second set is obtained after elimination of curvature and contains both wrinkle/line like features and fine textures. Third set is also obtained after elimination of curvature but consists of fine textures only. MGRF modeling for set I did not result in any visual similarity. Hence the curvature of underlying limbs cannot be modeled successfully and makes an inhomogeneous feature. For set 2 the wrinkle/line like features can be modeled with low/medium visual similarity depending on the spatial invariance. The results for set 3 show that fine textures of skin are almost always modeled successfully with medium/high visual similarity and make a homogeneous feature. We conclude that the MGRF model is able to model fine textures of skin successfully which are on scale of~ 0.1 mm. The surface profiles on this resolution can provide haptic sensation of roughness and friction. Therefore fine textures can be an important clue to different skin conditions perceived through tactile inspection via a haptic device

Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling

Position emission tomography (PET) is a powerful functional imaging modality with wide uses in fields such as oncology, cardiology, and neurology. Motivated by imaging datasets from a psoriasis clinical trial and a cohort of Alzheimer\u27s disease (AD) patients, several interesting methodological challenges were identified in various steps of quantitative analysis of PET data. In Chapter 1, we consider a classification scenario of bivariate thresholding of a predictor using an upper and lower cutpoints, as motivated by an image segmentation problem of the skin. We introduce a generalization of ROC analysis and the concept of the parameter path in ROC space of a classifier. Using this framework, we define the optimal ROC (OROC) to identify and assess performance of optimal classifiers, and describe a novel nonparametric estimation of OROC which simultaneous estimates the parameter path of the optimal classifier. In simulations, we compare its performance to alternative methods of OROC estimation. In Chapter 2, we develop a novel method to normalize PET images as an essential preprocessing step for quantitative analysis. We propose a method based on application of functional data analysis to image intensity distribution functions, assuming that that individual image density functions are variations from a template density. By modeling the warping functions using a modified function-on-scalar regression, the variations in density functions due to nuisance parameters are estimated and subsequently removed for normalization. Application to our motivating data indicate persistence of residual variations in standardized image densities. In Chapter 3, we propose a nonlinear mixed effects framework to model amyloid-beta (Aβ), an important biomarker in AD. We incorporate the hypothesized functional form of Aβ trajectory by assuming a common trajectory model for all subjects with variations in the location parameter, and a mixture distribution for the random effects of the location parameter address our empirical findings that some subjects may not accumulate Aβ. Using a Bayesian hierarchical model, group differences are specified into the trajectory parameters. We show in simulation studies that the model closely estimates the true parameters under various scenarios, and accurately estimates group differences in the age of onset

Circular Clustering with Polar Coordinate Reconstruction

There is a growing interest in characterizing circular data found in biological systems. Such data are wide ranging and varied, from signal phase in neural recordings to nucleotide sequences in round genomes. Traditional clustering algorithms are often inadequate due to their limited ability to distinguish differences in the periodic component. Current clustering schemes that work in a polar coordinate system have limitations, such as being only angle-focused or lacking generality. To overcome these limitations, we propose a new analysis framework that utilizes projections onto a cylindrical coordinate system to better represent objects in a polar coordinate system. Using the mathematical properties of circular data, we show our approach always finds the correct clustering result within the reconstructed dataset, given sufficient periodic repetitions of the data. Our approach is generally applicable and adaptable and can be incorporated into most state-of-the-art clustering algorithms. We demonstrate on synthetic and real data that our method generates more appropriate and consistent clustering results compared to standard methods. In summary, our proposed analysis framework overcomes the limitations of existing polar coordinate-based clustering methods and provides a more accurate and efficient way to cluster circular data.Comment: Manuscript is under review in IEEE Transactions on Computational Biology and Bioinformatics. Copyright holder is credited to IEE

PMNs, naRNA-LL37 complexes and platelets - a vicious inflammatory 'trio' in psoriasis

Psoriasis is an autoinflammatory skin disease with high incidence (3% of adults) in Western countries, accompanied by personal and socioeconomic burden. Psoriatic skin lesions are characterized by hyperproliferating keratinocytes, by vasodilatation in the dermis and most importantly by skin infiltration of leukocytes, dominated by neutrophils (PMNs)

MALT1 protease function – from ubiquitin binding to substrate cleavage

The protease MALT1 has a key function in the activation of lymphocytes and the regulation of the immune response, by promoting the activation of pro-inflammatory transcription factors such as NF-B. Dysregulation of MALT1 is implicated in immunodeficiency, autoimmune diseases, lymphomagenesis and non-lymphoid malignancies. Therefore, MALT1 proteolytic activity has appeared as a possible pharmaceutical target, however, the precise mechanism of MALT1 protease activation and the role of individual substrate cleavage events remain incompletely defined. Thus, the aims of this study are to firstly elucidate the molecular mechanism of MALT1 activation and second, to explore the role of the MALT1-dependent cleavage of one particular substrate namely, A20. The protease activity of MALT1 is tightly controlled by conjugation of monoubiquitin to its third auto-inhibitory Ig-like domain, but the mechanism governing the release of the protease domain by a single ubiquitin moiety remains unknown. Here, we identify the Ig3 domain of MALT1 as a novel ubiquitin-binding domain, responsible for MALT1 monoubiquitination, which is essential for MALT1 proteolytic activity and lymphocyte activation. Furthermore, we reveal an allosteric communication from the monoubiquitination site through the protease-Ig3 interaction surface to the catalytic active site of the protease domain. One of the first substrates of MALT1 that has been identified is A20, a potent anti-inflammatory protein. A20 is a well-described negative regulator of the NF-B signaling pathway downstream of different pro-inflammatory stimuli and a regulator of cell death. Although MALT1-dependent A20 cleavage is generally thought to promote NF-B activity, the functional role of A20 cleavage remains controversial and not well defined. This is due to the fact that the originally described single cleavage site in A20 is not conserved among species and only a small proportion of cellular A20 undergoes cleavage. Here, we demonstrate that MALT1 cleaves A20 at a total of four distinct sites in B and T lymphocytes, including three novel cleavage sites with unusual sequence properties, which are conserved in the mouse and other species. The cleavage fragments lost their capacity to regulate the NF-B pathway, but are stable within the cell, suggesting that they retain an unknown physiological function in lymphocytes. Collectively, our findings provide fundamentally new insights into the mechanism of MALT1 protease activation and its cleavage site specificity, and suggest that MALT1-dependent A20 cleavage has roles that go beyond the enhancement of NF-B activity. -- La protéase MALT1 a une fonction clé dans l’activation des lymphocytes et la régulation de la réponse immunitaire, en favorisant l’activation de facteurs de transcription pro- inflammatoires comme le NF-B. La dérégulation de MALT1 est impliquée dans l’immunodéficience, les maladies auto-immunes, la lymphomagénèse et les tumeurs malignes non-lymphoides. Par conséquent, l’activité protéolytique de MALT1 est apparue comme une cible thérapeutique possible, cependant le mécanisme précis de l’activation de la protéase MALT1 et le rôle du clivage de chacun de ses substrats restent en partie incompris. Ainsi les objectifs de cette étude sont d’abord d’élucider le mécanisme moléculaire d’activation de MALT1 puis d’explorer le rôle du clivage dépendant de MALT1 d’un substrat en particulier nommé A20. L’activité protéase de MALT1 est étroitement contrôlée par la conjugaison d’une monoubiquitine à son troisième domaine auto-inhibiteur de type Ig, mais le mécanisme régissant la libération du domaine protéase par un seul fragment d'ubiquitine reste inconnu. Ici, nous avons identifié le domaine Ig3 de MALT1 comme un nouveau domaine de liaison à l’ubiquitine, responsable de la monoubiquitination de MALT1, qui est essentielle pour son activité protéolytique et l’activation lymphocytaire. De plus, nous avons révélé une transmission allostérique du site de monoubiquitination à travers la surface d’interaction protéase-Ig3 au site catalytique actif du domaine protéase. L’un des premiers substrats de MALT1 qui a été identifié est A20, une protéine anti-inflammatoire puissante. A20 est un régulateur négatif bien décrit de la voie de signalisation NF-B en aval de différents stimuli pro-inflammatoires et un régulateur de la mort cellulaire. Bien que le clivage de A20 dépendant de MALT1 soit généralement vu comme un promoteur de l’activité de NF-B, le rôle fonctionnel du clivage de A20 reste controversé et mal défini. Cela est dû au fait que le site de clivage originellement découvert de A20 n’est pas conservé chez les autres espèces et que seulement une petite partie de la protéine cellulaire est clivée. Ici, nous démontrons que MALT1 clive A20 sur un total de quatre sites distincts dans les lymphocytes B et T. Nous avons également identifié trois nouveaux sites de clivage avec des motifs inhabituels, sites qui sont conservés chez la souris et d’autres espèces. Les fragments issus du clivage ont perdu leur capacité à réguler la voie NF-B, mais sont stables dans la cellule, suggérant qu’ils gardent une fonction physiologique inconnue dans les lymphocytes. Ensemble, nos résultats apportent de nouvelles informations fondamentales sur le mécanisme d’activation de la protéase MALT1 et sur la spécificité de ses sites de clivage, et suggèrent que le clivage de A20 dépendant de MALT1 a un rôle qui va au- delà d’une simple activation de NF-B