Discriminating Basal Cell Carcinoma and Bowen’s Disease with Novel Hyperspectral Imaging System and Convolutional Neural Networks

Delineating margins of lentigo maligna using a hyperspectral imaging system

Lentigo maligna (LM) is an in situ form of melanoma which can progress into invasive lentigo maligna melanoma (LMM). Variations in the pigmentation and thus visibility of the tumour make assessment of lesion borders challenging. We tested hyperspectral imaging system (HIS) in in vivo preoperative delineation of LM and LMM margins. We compared lesion margins delineated by HIS with those estimated clinically, and confirmed histologically. A total of 14 LMs and 5 LMMs in 19 patients were included. HIS analysis matched the histopathological analysis in 18/19 (94.7%) cases while in 1/19 (5.3%) cases HIS showed lesion extension not confirmed by histopathology (false positives). Compared to clinic…

S2k guideline for treatment of cutaneous lupus erythematosus - guided by the European Dermatology Forum (EDF) in cooperation with the European Academy of Dermatology and Venereology (EADV)

Cutaneous lupus erythematosus (CLE) is a rare inflammatory autoimmune disease with heterogeneous clinical manifestations. To date, no therapeutic agents have been licensed specifically for patients with this disease entity, and topical and systemic drugs are mostly used 'off-label'. The aim of the present guideline was to achieve a broad consensus on treatment strategies for patients with CLE by a European subcommittee, guided by the European Dermatology Forum (EDF) and supported by the European Academy of Dermatology and Venereology (EADV). In total, 16 European participants were included in this project and agreed on all recommendations. Topical corticosteroids remain the mainstay of trea…

Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma

Differentiating Malignant from Benign for Melanocytic and Non-melanocytic Skin Tumors : A Pilot Study on Hyperspectral Imaging and Convolutional Neural Networks

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface mo…

FPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pé…

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasi…