Bridging the Gap Between Vitiligo Segmentation and Clinical Scores

Quantitative evaluation of vitiligo is crucial for assessing treatment response. Dermatologists evaluate vitiligo regularly to adjust their treatment plans, which requires extra work. Furthermore, the evaluations may not be objective due to inter- and intra-assessor variability. Though automatic vitiligo segmentation methods provide an objective evaluation, previous methods mainly focus on patch-wise images, and their results cannot be translated into clinical scores for treatment adjustment. Thus, full-body vitiligo segmentation needs to be developed for recording vitiligo changes in different body parts of a patient and for calculating the clinical scores. To bridge this gap, the first full-body vitiligo dataset with 1740 images, following the international vitiligo photo standard, was established. Compared with patch-wise images, full-body images have more complicated ambient light conditions and larger variances in lesion size and distribution. Additionally, in some hand and foot images, skin can be fully covered by either vitiligo or healthy skin. Previous patch-wise segmentation studies completely ignore these cases, as they assume that the contrast between vitiligo and healthy skin is available in each image for segmentation. To address the aforementioned challenges, the proposed algorithm in this study exploits a tailor-made contrast enhancement scheme and long-range comparison. Furthermore, a novel confidence score refinement module is proposed to manage images fully covered by vitiligo or healthy skin. Our results can be converted to clinical scores and used by clinicians. Compared to the state-of-the-art method, the proposed algorithm reduces the average per-image vitiligo involvement percentage error from 3.69% to 1.81%, and the top 10% per-image errors from 23.17% to 8.29%. Our algorithm achieves 1.17% and 3.11% for the mean and max error for the per-patient vitiligo involvement percentage, which is better than an experienced dermatologist's naked-eye evaluation.

Poisson-based image editing for semi-supervised vitiligo lesion segmentation with limited annotations

Vitiligo lesion segmentation is crucial for the assessment and treatment of vitiligo. There are two significant challenges in this problem, namely, the availability of dense segmentation annotations and the collection of large amounts of vitiligo images, which are also major challenges in medical image analysis (MIA). However, most existing methods often heavily rely on the availability of large-scale labeled datasets and high-quality annotations. Consequently, the performance of these models may not be easily reproducible or transferable to those domains with limited data availability. As a result, there is a need to develop alternative approaches that can leverage unlabeled datasets for segmentation with a small-scale training set. In this paper, we propose a data augmentation strategy based on image editing, which can synthesize a large number of samples using a small number of annotated data. The synthesized examples are of high visual quality and enforce the segmentation performance without any cost. Besides, we also adapt the Mean-Teacher framework for reliable predictions mining from unlabeled samples to alleviate the demands of densely annotated segmentations. We obtain pseudo-labels for unlabeled samples by utilizing highly confident pixels. On the other hand, we proposed a new Bimodal Vitiligo Lesions Segmentation (BVLS) dataset containing fine-grain segmentation masks and bimodal images usually used for vitiligo diagnosis to mitigate the lack of a vitiligo segmentation dataset. Extensive experiments conducted on the BLVS dataset demonstrate that our approach can achieve significant improvements (+17.27%) compared with previous data augmentation methods on the UNet backbone. Furthermore, the semi-supervised framework can reach an IoU of 49.71% with only 10% annotated images. Our code and dataset are availabel at https://github.com/JcWang20/BLVS.

Segmenting Vitiligo on Clinical Face Images Using CNN Trained on Synthetic and Internet Images

Accurately diagnosing and describing the severity of vitiligo is crucial for prognostication, treatment selection and comparison. Currently, disease severity scores require dermatologists to estimate percentage area of involvement, which is subjected to inter and intra-assessor variability. Previous studies focus on pure skin but vitiligo on the face, which has a more serious impact on patients' quality of life, was completely neglected. Convolutional neural networks (CNNs) have good performance on many segmentation tasks. However, due to data privacy, it is hard to have a large clinical vitiligo face image dataset to train a CNN. To address this challenge, images from two different sources, the Internet and the proposed vitiligo face synthesis algorithm, are employed in training. 843 vitiligo images taken from different viewpoints were collected from the Internet. These images are hugely different from the target clinical images collected according to a newly established international standard. To have more vitiligo face images similar to the target clinical images to enhance segmentation performance, an image synthesis algorithm is proposed. Both synthetic and Internet images are used to train a CNN which is modified from the fully convolutional network (FCN) to segment face vitiligo lesions. The results show that 1) the synthetic images effectively improve segmentation performance; 2) the proposed algorithm achieves 1.06 % error for the face vitiligo area estimation and 3) it is more accurate than two dermatologists and all the previous automated vitiligo segmentation methods, which were designed for segmentation vitiligo on pure skin.

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

BACKGROUND

Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules and patches which brings many challenges for the patients suffering from. For vitiligo severity assessment, several scoring methods have been proposed based on morphometry and colorimetry. But, all methods suffer from much inter- and intra-observer variations for estimating the depigmented area. For all mentioned assessment methods of vitiligo disorder, accurate segmentation of the skin images for lesion detection and localization is required. The image segmentation for localizing vitiligo skin lesions has many challenges because of illumination variation, different shapes and sizes of vitiligo lesions, vague lesion boundaries and skin hairs and vignette effects. The manual image segmentation is a tedious and time-consuming task. Therefore, using automatic image segmentation methods for lesion detection is necessarily required.

MATERIALS AND METHODS

In this study, a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) methods is proposed for localizing vitiligo lesions in skin images. Unsupervised segmentation methods do not require prior manual segmentation of vitiligo lesions which is a tedious and time-consuming task with intra- and inter-observer variations.

RESULTS

Our collected dataset includes 877 images taken from 21 patients with the resolution of 5760*3840 pixels suffering from vitiligo disorder. Experimental results show that SEDCIS outperforms the compared methods with accuracy of 97%, sensitivity of 98%, specificity of 96%, area overlapping of 94%, and Dice index of 97%.

CONCLUSION

The proposed method can segment vitiligo lesions with highly reasonable performance and can be used for assessing the vitiligo lesion surface.

In vivo third-harmonic generation microscopy study on vitiligo patients

Melanin is known to provide strong third-harmonic generation (THG) contrast in human skin. With a high concentration in basal cell cytoplasm, THG contrast provided by melanin overshadows other THG sources in human skin studies. For better understanding of the THG signals in keratinocytes without the influence of melanin, an in vivo THG microscopy (THGM) study was first conducted on vitiliginous skin. As a result, the THG-brightness ratio between the melanin-lacking cytoplasm of basal cells and collagen fibers is about 1.106 at the dermal-epidermal junctions of vitiliginous skin, indicating high sensitivity of THGM for the presence of melanin. We further applied the in vivo THGM to assist evaluating the therapeutic outcome from the histopathological point of view for those showed no improvement under narrowband ultraviolet B therapy based on the seven-point Physician Global Assessment score. Our clinical study indicates the high potential of THGM to assist the histopathological assessment of the therapeutic efficacy of vitiligo treatments.

Vitiligo of the Face in a Patient Treated With Vemurafenib for Metastatic Melanoma

Vemurafenib is a potent and selective BRAF inhibitor, which is effective on patients with BRAF V600E mutated late-stage melanoma. Common and less common adverse skin reactions include photosensitivity, maculo-papular exanthema, hand-foot skin reactions, hyperkeratotic follicular rash, pruritus, benign verrucous papillomas, plantar hyperkeratosis, keratoacanthomas, squamous cell carcinomas, infections, and melanoma. To our knowledge, vitiligo has been reported in 2 cases only. This paper reports the case of a 63-year-old man with metastatic melanoma, who developed sudden facial depigmentation after 4 weeks of treatment with vemurafenib 960 mg twice daily. Features consistent with vitiligo were evident at clinical and ultraviolet light examination, as well as at in vivo reflectance confocal microscopy. The latter examination showed lack of normal brightly refractile papillary rings at the dermo-epidermal junction in lesional skin, as well as decreased brightness and half-rings with "scalloped border-like" features in adjacent non-lesional skin. Vitiligo is an adverse reaction to be expected in patients treated with vemurafenib and whether its occurrence may be associated with a positive outcome, as suggested by previous investigations, is still a matter of debate. <br /><br /> <em>J Drugs Dermatol. </em>2016;15(6):766-768.

The liver and its ultrasonographic picture in children suffering from vitiligo

The size and the structure of the liver in 38 children with vitiligo and 43 healthy children suffering from a mild form of pityriasis capitis were measured in an ultrasonographic examination and then compared. In the examined group there were found single cases beyond the norm. No changes in the structure of the liver parenchyma were stated in any of the vitiligo cases. For the liver measured in L1 there were stated 94.7% cases of vitiligo within the range from 2.5 to 97.5 percentile values in the control group. For the liver measured in L2--92.5% cases from 2.5 to 97.5 percentile values in the control group and for the liver measured in L3--92.1% cases of vitiligo were within the range 2.5 do 97.5 percentile values in the control group.

Radio-active iodine uptake in vitiligo

Vitiligo and thyroid disease are commonly associated disorders. Twenty-two clinically euthyroid vitiligo patients were studied for functional assessment of thyroid by radioactive iodine uptake assay. Half of them showed abnormal uptake values at 24 hours. Of these patients, 90% had lower values indicating a tendency towards developing hypothyroid state. Subclinical thyroid dysfunction in vitiligo appears to be an adaptive change.