A novel automated approach to rapid and precise in vivo measurement of hair morphometrics using a smartphone.

BACKGROUND: Although many hair disorders can be readily diagnosed based on their clinical appearance, their progression and response to treatment are often difficult to monitor, particularly in quantitative terms. We introduce an innovative technique utilizing a smartphone and computerized image analysis to expeditiously and automatically measure and compute hair density and diameter in patients in real time. METHODS: A smartphone equipped with a dermatoscope lens wirelessly transmits trichoscopy images to a computer for image processing. A black-and-white binary mask image representing hair and skin is produced, and the hairs are thinned into single-pixel-thick fiber skeletons. Further analysis based on these fibers allows morphometric characteristics such as hair shaft number and diameters to be computed rapidly. The hair-bearing scalps of fifty participants were imaged to assess the precision of our automated smartphone-based device in comparison with a specialized trichometry device for hair shaft density and diameter measurement. The precision and operation time of our technique relative to manual trichometry, which is commonly used by hair disorder specialists, is determined. RESULTS: An equivalence test, based on two 1-sided t tests, demonstrates statistical equivalence in hair density and diameter values between this automated technique and manual trichometry within a 20% margin. On average, this technique actively required 24 seconds of the clinician's time whereas manual trichometry necessitated 9.2 minutes. CONCLUSION: Automated smartphone-based trichometry is a rapid, precise, and clinically feasible technique which can significantly facilitate the assessment and monitoring of hair loss. Its use could be easily integrated into clinical practice to improve standard trichoscopy.

Assessment of Facial Morphologic Features in Patients With Congenital Adrenal Hyperplasia Using Deep Learning.

Importance: Congenital adrenal hyperplasia (CAH) is the most common primary adrenal insufficiency in children, involving excess androgens secondary to disrupted steroidogenesis as early as the seventh gestational week of life. Although structural brain abnormalities are seen in CAH, little is known about facial morphology. Objective: To investigate differences in facial morphologic features between patients with CAH and control individuals with use of machine learning. Design, Setting, and Participants: This cross-sectional study was performed at a pediatric tertiary center in Southern California, from November 2017 to December 2019. Patients younger than 30 years with a biochemical diagnosis of classical CAH due to 21-hydroxylase deficiency and otherwise healthy controls were recruited from the clinic, and face images were acquired. Additional controls were selected from public face image data sets. Main Outcomes and Measures: The main outcome was prediction of CAH, as performed by machine learning (linear discriminant analysis, random forests, deep neural networks). Handcrafted features and learned representations were studied for CAH score prediction, and deformation analysis of facial landmarks and regionwise analyses were performed. A 6-fold cross-validation strategy was used to avoid overfitting and bias. Results: The study included 102 patients with CAH (62 [60.8%] female; mean [SD] age, 11.6 [7.1] years) and 59 controls (30 [50.8%] female; mean [SD] age, 9.0 [5.2] years) from the clinic and 85 controls (48 [60%] female; age, <29 years) from face databases. With use of deep neural networks, a mean (SD) AUC of 92% (3%) was found for accurately predicting CAH over 6 folds. With use of classical machine learning and handcrafted facial features, mean (SD) AUCs of 86% (5%) in linear discriminant analysis and 83% (3%) in random forests were obtained for predicting CAH over 6 folds. There was a deviation of facial features between groups using deformation fields generated from facial landmark templates. Regionwise analysis and class activation maps (deep learning of regions) revealed that the nose and upper face were most contributory (mean [SD] AUC: 69% [17%] and 71% [13%], respectively). Conclusions and Relevance: The findings suggest that facial morphologic features in patients with CAH is distinct and that deep learning can discover subtle facial features to predict CAH. Longitudinal study of facial morphology as a phenotypic biomarker may help expand understanding of adverse lifespan outcomes for patients with CAH.

Multi-site harmonization of diffusion MRI data in a registration framework.

Diffusion MRI (dMRI) data acquired on different scanners varies significantly in its content throughout the brain even if the acquisition parameters are nearly identical. Thus, proper harmonization of such data sets is necessary to increase the sample size and thereby the statistical power of neuroimaging studies. In this paper, we present a novel approach to harmonize dMRI data (the raw signal, instead of dMRI derived measures such as fractional anisotropy) using rotation invariant spherical harmonic (RISH) features embedded within a multi-modal image registration framework. All dMRI data sets from all sites are registered to a common template and voxel-wise differences in RISH features between sites at a group level are used to harmonize the signal in a subject-specific manner. We validate our method on diffusion data acquired from seven different sites (two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across these sites before and after data harmonization. Validation was also done on a group oftest subjects, which were not used to "learn" the harmonization parameters. We also show results using TBSS before and after harmonization for independent validation of the proposed methodology. Using synthetic data, we show that any abnormality in diffusion measures due to disease is preserved during the harmonization process. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences in the signal can be removed using the proposed method in a model independent manner.

Randomized trial of calcipotriol combined with 5-fluorouracil for skin cancer precursor immunotherapy.

BACKGROUND: Actinic keratosis is a precursor to cutaneous squamous cell carcinoma. Long treatment durations and severe side effects have limited the efficacy of current actinic keratosis treatments. Thymic stromal lymphopoietin (TSLP) is an epithelium-derived cytokine that induces a robust antitumor immunity in barrier-defective skin. Here, we investigated the efficacy of calcipotriol, a topical TSLP inducer, in combination with 5-fluorouracil (5-FU) as an immunotherapy for actinic keratosis. METHODS: The mechanism of calcipotriol action against skin carcinogenesis was examined in genetically engineered mouse models. The efficacy and safety of 0.005% calcipotriol ointment combined with 5% 5-FU cream were compared with Vaseline plus 5-FU for the field treatment of actinic keratosis in a randomized, double-blind clinical trial involving 131 participants. The assigned treatment was self-applied to the entirety of the qualified anatomical sites (face, scalp, and upper extremities) twice daily for 4 consecutive days. The percentage of reduction in the number of actinic keratoses (primary outcome), local skin reactions, and immune activation parameters were assessed. RESULTS: Calcipotriol suppressed skin cancer development in mice in a TSLP-dependent manner. Four-day application of calcipotriol plus 5-FU versus Vaseline plus 5-FU led to an 87.8% versus 26.3% mean reduction in the number of actinic keratoses in participants (P < 0.0001). Importantly, calcipotriol plus 5-FU treatment induced TSLP, HLA class II, and natural killer cell group 2D (NKG2D) ligand expression in the lesional keratinocytes associated with a marked CD4+ T cell infiltration, which peaked on days 10-11 after treatment, without pain, crusting, or ulceration. CONCLUSION: Our findings demonstrate the synergistic effects of calcipotriol and 5-FU treatment in optimally activating a CD4+ T cell-mediated immunity against actinic keratoses and, potentially, cancers of the skin and other organs. TRIAL REGISTRATION: ClinicalTrials.gov NCT02019355. FUNDING: Not applicable (investigator-initiated clinical trial).

Skin lesion tracking using structured graphical models.

An automatic pigmented skin lesions tracking system, which is important for early skin cancer detection, is proposed in this work. The input to the system is a pair of skin back images of the same subject captured at different times. The output is the correspondence (matching) between the detected lesions and the identification of newly appearing and disappearing ones. First, a set of anatomical landmarks are detected using a pictorial structure algorithm. The lesions that are located within the polygon defined by the landmarks are identified and their anatomical spatial contexts are encoded by the landmarks. Then, these lesions are matched by labeling an association graph using a tensor-based algorithm. A structured support vector machine is employed to learn all free parameters in the aforementioned steps. An adaptive learning approach (on-the-fly vs offline learning) is applied to set the parameters of the matching objective function using the estimated error of the detected landmarks. The effectiveness of the different steps in our framework is validated on 194 skin back images (97 pairs).

Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners.

Harmonizing diffusion MRI (dMRI) images across multiple sites is imperative for joint analysis of the data to significantly increase the sample size and statistical power of neuroimaging studies. In this work, we develop a method to harmonize diffusion MRI data across multiple sites and scanners that incorporates two main novelties: i) we take into account the spatial variability of the signal (for different sites) in different parts of the brain as opposed to existing methods, which consider one linear statistical covariate for the entire brain; ii) our method is model-free, in that no a-priori model of diffusion (e.g., tensor, compartmental models, etc.) is assumed and the signal itself is corrected for scanner related differences. We use spherical harmonic basis functions to represent the signal and compute several rotation invariant features, which are used to estimate a regionally specific linear mapping between signal from different sites (and scanners). We validate our method on diffusion data acquired from four different sites (including two GE and two Siemens scanners) on a group of healthy subjects. Diffusion measures such fractional anisotropy, mean diffusivity and generalized fractional anisotropy are compared across multiple sites before and after the mapping. Our experimental results demonstrate that, for identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge.

Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Hair enhancement in dermoscopic images using dual-channel quaternion tubularness filters and MRF-based multilabel optimization.

Hair occlusion is one of the main challenges facing automatic lesion segmentation and feature extraction for skin cancer applications. We propose a novel method for simultaneously enhancing both light and dark hairs with variable widths, from dermoscopic images, without the prior knowledge of the hair color. We measure hair tubularness using a quaternion color curvature filter. We extract optimal hair features (tubularness, scale, and orientation) using Markov random field theory and multilabel optimization. We also develop a novel dual-channel matched filter to enhance hair pixels in the dermoscopic images while suppressing irrelevant skin pixels. We evaluate the hair enhancement capabilities of our method on hair-occluded images generated via our new hair simulation algorithm. Since hair enhancement is an intermediate step in a computer-aided diagnosis system for analyzing dermoscopic images, we validate our method and compare it to other methods by studying its effect on: 1) hair segmentation accuracy; 2) image inpainting quality; and 3) image classification accuracy. The validation results on 40 real clinical dermoscopic images and 94 synthetic data demonstrate that our approach outperforms competing hair enhancement methods.

Spatial normalization of human back images for dermatological studies.

A large number of pigmented skin lesions (PSLs) are a strong predictor of malignant melanoma. Many dermatologists advocate total body photography for high-risk patients because detecting new-appearing, disappearing, and changing PSL is important for early detection of the disease. However, manual inspection and matching of PSL is a subjective, tedious, and error-prone task. A computer program for tracking the corresponding PSL will greatly improve the matching process. In this paper, we describe the construction of the first human back template (atlas), which is used to facilitate spatial normalization of the PSL during the matching process. Four pairs of anatomically meaningful landmarks (neck, shoulder, armpit, and hip points) are used as reference points on the back image. Using the landmarks, a grid with longitudes and latitudes is constructed and overlaid on each subject-specific back image. To perform spatial normalization, the grid is registered into the back template, a unit-square rectilinear grid. To demonstrate the benefits of using the back template, we apply several state-of-the-art point-matching algorithms on 56 pairs of real dermatological images and show that utilizing spatially normalized coordinates improves the PSL matching accuracies.

Fast and robust 3D vertebra segmentation using statistical shape models.

We propose a top-down fully automatic 3D vertebra segmentation algorithm using global shape-related as well as local appearance-related prior information. The former is brought into the system by a global statistical shape model built from annotated training data, i.e., annotated CT volumes. The latter is handled by a machine learning-based component, i.e., a boundary detector, providing a strong discriminative model for vertebra surface appearance by making use of local context-encoding features. This boundary detector, which is essentially a probabilistic boosting-tree classifier, is also learnt from annotated training data. Contextual information is taken into account by representing vertebra surface candidate voxels with high-dimensional vectors of 3D steerable features derived from the observed volume intensities. Our system does not only consider the body of the individual vertebrae but also the spinal processes. Before segmentation, the image parts depicting individual vertebrae are spatially normalized with respect to their bounding box information in terms of translation, orientation, and scale leading to more accurate results. We evaluate segmentation accuracy on 7 CT volumes each depicting 22 vertebrae. The results indicate a symmetric point-to-mesh surface error of 1.37 ± 0.37 mm, which matches the current state-of-the-art.

Uncertainty-based feature learning for skin lesion matching using a high order MRF optimization framework.

We formulate the pigmented-skin-lesion (PSL) matching problem as a relaxed labeling of an association graph. In this graph labeling problem, each node represents a mapping between a PSL from one image to a PSL in the second image and the optimal labels are those optimizing a high order Markov Random Field energy (MRF). The energy is made up of unary, binary, and ternary energy terms capturing the likelihood of matching between the points, edges, and cliques of two graphs representing the spatial distribution of the two PSL sets. Following an exploration of various MRF energy terms, we propose a novel entropy energy term encouraging solutions with low uncertainty. By interpreting the relaxed labeling as a measure of confidence, we further leverage the high confidence matching to sequentially constrain the learnt objective function defined on the association graph. We evaluate our method on a large set of synthetic data as well as 56 pairs of real dermatological images. Our proposed method compares favorably with the state-of-the-art.