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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...

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Autores principales: Lindholm, Vivian, Raita-Hakola, Anna-Maria, Annala, Leevi, Salmivuori, Mari, Jeskanen, Leila, Saari, Heikki, Koskenmies, Sari, Pitkänen, Sari, Pölönen, Ilkka, Isoherranen, Kirsi, Ranki, Annamari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999463/
https://www.ncbi.nlm.nih.gov/pubmed/35407522
http://dx.doi.org/10.3390/jcm11071914
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author Lindholm, Vivian
Raita-Hakola, Anna-Maria
Annala, Leevi
Salmivuori, Mari
Jeskanen, Leila
Saari, Heikki
Koskenmies, Sari
Pitkänen, Sari
Pölönen, Ilkka
Isoherranen, Kirsi
Ranki, Annamari
author_facet Lindholm, Vivian
Raita-Hakola, Anna-Maria
Annala, Leevi
Salmivuori, Mari
Jeskanen, Leila
Saari, Heikki
Koskenmies, Sari
Pitkänen, Sari
Pölönen, Ilkka
Isoherranen, Kirsi
Ranki, Annamari
author_sort Lindholm, Vivian
collection PubMed
description 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.
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spelling pubmed-89994632022-04-12 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 Lindholm, Vivian Raita-Hakola, Anna-Maria Annala, Leevi Salmivuori, Mari Jeskanen, Leila Saari, Heikki Koskenmies, Sari Pitkänen, Sari Pölönen, Ilkka Isoherranen, Kirsi Ranki, Annamari J Clin Med Article 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. MDPI 2022-03-30 /pmc/articles/PMC8999463/ /pubmed/35407522 http://dx.doi.org/10.3390/jcm11071914 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lindholm, Vivian
Raita-Hakola, Anna-Maria
Annala, Leevi
Salmivuori, Mari
Jeskanen, Leila
Saari, Heikki
Koskenmies, Sari
Pitkänen, Sari
Pölönen, Ilkka
Isoherranen, Kirsi
Ranki, Annamari
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999463/
https://www.ncbi.nlm.nih.gov/pubmed/35407522
http://dx.doi.org/10.3390/jcm11071914
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