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利用直接照明多光谱成像对黑素细胞病变进行分类。

Classification of melanocytic lesions using direct illumination multispectral imaging.

机构信息

Department of Dermatology, University Hospital of Basel, Basel, Switzerland.

Imaging and Sensing Department, Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Strasse 7, 07745, Jena, Germany.

出版信息

Sci Rep. 2024 Aug 16;14(1):19036. doi: 10.1038/s41598-024-69773-x.

DOI:10.1038/s41598-024-69773-x
PMID:39152181
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11329730/
Abstract

With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.

摘要

随着黑色素瘤发病率和死亡率的上升,早期发现和手术切除原发性病变至关重要。多光谱成像技术是一种新的非侵入性技术,通过测量生物组织的反射光谱,可以促进皮肤癌的检测。目前,由于表面反射率高,入射照明允许很少的光从更深的皮肤层反射回来。巴塞尔大学医院进行了一项试点研究,以评估直接光耦合的多光谱成像是否可以从更深的皮肤层提取更多信息,以便更准确地对黑色素瘤病变进行分类。该研究共纳入了 23 名患者的 27 个可疑色素性病变(6 个黑色素瘤、6 个发育不良痣、12 个黑色素细胞痣、3 个其他病变)。使用带有入射和直接照明的原型快照镶嵌多光谱相机在切除前对病变进行成像,随后使用经过预训练的多光谱图像分析模型对病变进行分类。与组织病理学检查确定的尊严相比,使用入射光的灵敏度为 83.3%,特异性为 58.8%。直接光耦合的灵敏度为 100%,特异性为 82.4%。对应红色、绿色和蓝色病变图像的卷积神经网络分类的灵敏度比直接光耦合的多光谱图像分类低 16.7%(83.3%,6 个恶性病变中有 5 个被检测到),特异性低 20.9%(61.5%)。我们的结果表明,将直接光多光谱成像纳入黑色素瘤检测过程中,可能会提高尊严分类的准确性。这种新评估的照明方法可以提高多光谱在皮肤癌检测中的应用。需要进一步进行更大规模的研究来验证相机原型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/793a8a3c93ef/41598_2024_69773_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/4d98c4af23c8/41598_2024_69773_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/12a283f07daf/41598_2024_69773_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/c5c003828bed/41598_2024_69773_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/314654689835/41598_2024_69773_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/793a8a3c93ef/41598_2024_69773_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/4d98c4af23c8/41598_2024_69773_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/12a283f07daf/41598_2024_69773_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/c5c003828bed/41598_2024_69773_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/314654689835/41598_2024_69773_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f912/11329730/793a8a3c93ef/41598_2024_69773_Fig5_HTML.jpg

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Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?在真实环境下,二维和三维全身摄影及卷积神经网络的人工智能在黑色素瘤高危人群中的诊断性能:皮肤癌筛查的新纪元?
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