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

Vivian Lindholm (Corresponding Author), Anna Maria Raita-Hakola (Corresponding Author), Leevi Annala, Mari Salmivuori, Leila Jeskanen, Heikki Saari, Sari Koskenmies, Sari Pitkänen, Ilkka Pölönen, Kirsi Isoherranen, Annamari Ranki

Research output: Contribution to journalArticleScientificpeer-review

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 skinsurface 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 nonpigmented 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.

Original languageEnglish
Article number1914
JournalJournal of Clinical Medicine
Volume11
Issue number7
DOIs
Publication statusPublished - 30 Mar 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • biomedical optical imaging
  • convolutional neural networks
  • hyperspectral imaging
  • non-invasive imaging
  • optical modelling
  • photometric stereo
  • skin cancer
  • skin imaging

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