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

Abstract

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 models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces.

Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth Camera

“…Figure 10b depicts the splitting plane and the related vectors. Equation (10) gives us the signed emission angle.…”

“…At close proximity, depth data of complex surfaces can be inferred through controlled illumination of the target and photometric stereo imaging. Skin cancer diagnosis is one medical imaging application of this setup employing a hyperspectral camera [10].…”

Estimating quantitative physiological and morphological tissue parameters of murine tumor models using hyperspectral imaging and optical profilometry

“…In recent years, HSI has been widely used to image human and animal tumors in brain [8,9,[11][12][13][14][15][16][17][18][19][20][21][22], breast [8,9,11,[23][24][25][26][27][28], colon [8,9,11,[29][30][31][32][33], prostate [8,9,11,34,35] and skin [8,9,11,[36][37][38][39][40][41][42]. The main shortcoming of most studies utilizing HSI is that they analyze images of histopathological slides to detect tumor cells ex vivo.…”

DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification

“…Currently, deep learning in medicine pertains mainly to clinical decision support and data analysis. By analyzing medical data for underlying patterns and relationships, deep learning systems have a broad range of applications, ranging from patient outcomes prediction 4-6 , diagnostics and classification [7][8][9][10] , and data segmentation 11,12 to the generation [13][14][15][16][17] and anonymization of datasets [18][19][20][21] with synthetic medical data.Policy and regulatory directives concerning medical data privacy and use continue to be updated globally. The US Health Insurance Portability and Accountability Act (HIPAA) 22 is similar to the General Data Protection Regulation (GDPR) 23 ; both were developed to restrict data flow and ascertain patient consent for health data dissemination.…”

“…Currently, deep learning in medicine pertains mainly to clinical decision support and data analysis. By analyzing medical data for underlying patterns and relationships, deep learning systems have a broad range of applications, ranging from patient outcomes prediction 4-6 , diagnostics and classification [7][8][9][10] , and data segmentation 11,12 to the generation [13][14][15][16][17] and anonymization of datasets [18][19][20][21] with synthetic medical data.…”