Can Multispectral Dermoscopy Help In Distinguishing Blue Color?

INTRODUCTION: The interpretation of colors is essential in the dermoscopic evaluation of skin lesions. The same blue color on white dermoscopy may indicate blood or pigment deep in the dermis. Contrary to white dermoscopy, multispectral dermoscopy uses different wavelengths of light to illuminate a lesion and is able to decompose the dermoscopic image into individual maps that allow to more clearly visualize specific skin structures such as pigment distribution (pigment map) and vasculature (blood map). These maps are called skin parameter maps. OBJECTIVES: The aim of this research is to investigate whether skin parameter maps can be used to objectively identify and distinguish the presence of pigment and blood, by using blue naevi and angiomas as models for respectively pigment and blood. METHODS: We retrospectively analyzed 24 blue naevi and 79 angiomas. The skin parameter maps of each of the lesions were independently reviewed by 3 expert dermoscopists, in the absence of the regular white-light dermoscopic image. RESULTS: All the observers provided high levels of diagnostic accuracy for blue naevus and angioma based on skin parameter maps alone, and the dermoscopic diagnosis was considered substantially reliable because of the 79% of diagnostic K agreement. Percentages of blue naevi and angiomas that showed respectively deep pigment and blood were very high at 95.8% and 97.5%. There was a percentage of lesions that counterintuitively showed blood in blue naevi (37.5%) and deep pigment in angiomas (28.8%). CONCLUSIONS: Skin parameter maps based on multispectral images can help to objectify the presence of deep pigment or blood in blue naevi and angiomas. The application of these skin parameter maps could help in the differential diagnosis between pigmented and vascular lesions.

Preoperative assessment of cutaneous melanoma thickness by multispectral dermoscopy.

Preoperative assessment of Breslow thickness by means of sonography and clinical and dermoscopic criteria in white light dermoscopy has been reported, but up until now, the use of multispectral dermoscopy has not been investigated. Aim of this research is to determine whether multispectral dermoscopy and more specifically pigment maps can be used as a predictive marker for Breslow thickness in melanoma. Pigment maps are generated in real time from multispectral dermoscopic images and help to visualize the presence of pigment in a lesion. Multispectral images of 110 melanomas were collected, using a digital handheld multispectral dermatoscope, and assessed independently by five observers for the presence or absence of deep pigment compared with the surrounding skin. According to histopathological examination, the mean Breslow thickness of all 110 melanomas was 1.04 mm (ranging from 0.1 to 14 mm). The group of melanomas where deep pigment was visualized on the multispectral image (n = 78) had a significantly higher Breslow thickness (1.19 mm) than the group where no deep pigment was observed (n = 32, mean Breslow 0.68 mm) (P = 0.025). This study is unique in preoperative assessment of tumour thickness by means of multispectral dermoscopy. Our data indicate that the presence of deep pigment as visualized in digital dermoscopic skin parameter maps identifies a group of thicker melanomas. Further prospective research is needed to validate these pigment maps, generated by multispectral dermoscopy as a measure to predict invasiveness in melanoma.

Data-Efficient Sensor Upgrade Path Using Knowledge Distillation.

Deep neural networks have achieved state-of-the-art performance in image classification. Due to this success, deep learning is now also being applied to other data modalities such as multispectral images, lidar and radar data. However, successfully training a deep neural network requires a large reddataset. Therefore, transitioning to a new sensor modality (e.g., from regular camera images to multispectral camera images) might result in a drop in performance, due to the limited availability of data in the new modality. This might hinder the adoption rate and time to market for new sensor technologies. In this paper, we present an approach to leverage the knowledge of a teacher network, that was trained using the original data modality, to improve the performance of a student network on a new data modality: a technique known in literature as knowledge distillation. By applying knowledge distillation to the problem of sensor transition, we can greatly speed up this process. We validate this approach using a multimodal version of the MNIST dataset. Especially when little data is available in the new modality (i.e., 10 images), training with additional teacher supervision results in increased performance, with the student network scoring a test set accuracy of 0.77, compared to an accuracy of 0.37 for the baseline. We also explore two extensions to the default method of knowledge distillation, which we evaluate on a multimodal version of the CIFAR-10 dataset: an annealing scheme for the hyperparameter α and selective knowledge distillation. Of these two, the first yields the best results. Choosing the optimal annealing scheme results in an increase in test set accuracy of 6%. Finally, we apply our method to the real-world use case of skin lesion classification.

Enhanced visualization of blood and pigment in multispectral skin dermoscopy.

BACKGROUND AND OBJECTIVES: Dermoscopy has proven its value in the diagnosis of skin cancer and, therefore, is well established in daily dermatology practice. Up until now, analogue white light dermoscopy is the standard. Multispectral dermoscopy is based on illumination of the skin with narrowband light sources with different wavelengths. Each of these wavelengths is differently absorbed by skin chromophores, such as pigment or (de)oxygenated blood. Multispectral dermoscopy could be a way to enhance the visualization of vasculature and pigment. We illustrate possible additional information by such "skin parameter maps" in some cases of basal cell carcinoma and Bowen's disease. METHODS: Using a new digital multispectral dermatoscope, skin images at multiple wavelengths are collected from different types of skin lesions. These particular images together with the knowledge on skin absorption properties, result in so called "skin parameter maps". RESULTS: A "pigment contrast map," which shows the relative concentration of primarily pigment, and a "blood contrast map" which shows the relative concentration of primarily blood were created. Especially, the latter is of importance in diagnosing keratinocyte skin cancer hence vascular structures are a characteristic feature, as further illustrated in the study. CONCLUSIONS: Skin parameter maps based on multispectral images can give better insight in the inner structures of lesions, especially in lesions with characteristic blood vessels such as Bowen's disease and basal cell carcinoma. Skin parameter maps can be used complementary to regular dermoscopy and could potentially facilitate diagnosing skin lesions.