Abstract LB227: Automated image registration and alignment facilitates assessment of change in pigmented lesions of patients at risk for melanoma

W. Maguire, Paul H. Haley, C. M. Dietz, M. Hoffelder, C. S. Brandt, Robin Joyce, Melissa D. Wilson, Darcy Ploucha, Christopher P Minnier, C. Sander, Hong Wang, H. Zarour, K. Mitchell, Ellen K. Hughes, J. Kirkwood
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Abstract

Introduction: Melanoma develops either de novo or from non-obligate precursor lesions known as atypical/dysplastic nevi. Assessment of change in number and morphology of pigmented cutaneous lesions over time is critical to early detection of skin cancers and may provide preliminary signals of efficacy in early phase therapeutic prevention trials for melanoma. Despite the use of total body digital photography for at least 20 years to document the presence of these lesions, as well as recent progress in computer-aided diagnosis of lesions in clinical images, automated methods to characterize the evolution of skin lesions are still lacking. The purpose of this study was to develop and validate a computer vision approach to facilitate detection and quantification of changes in nevi in serial digital photographs. Methods: The ‘DermaViz9 mathematical algorithms were developed to register nevi between sequential images and to align images for improved comparison. The technique is based on the bispectrum algorithm, modified to adapt for human skin changes. Adaptive normalization techniques adjust for lighting and skin tone variations. Warping and shear of skin are accommodated by a hierarchical iteration of these algorithms coupled with probabilistic matching techniques for accurate alignment. The technology allows both for improved qualitative comparison by clinicians when the aligned images are toggled between dates, and for digital quantification of changes in (a)symmetry, (b)order, (c)olor, and (d)iameter of the lesions. In this pilot study, serial posterior truncal photographs from 17 patients with multiple atypical nevi and a history of melanoma were obtained from a pre-existing image and nevus biobanking protocol database at our institution. De-identified images were processed and analyzed with DermaViz software, and results were validated by a panel of Melanoma Program clinicians. Results: DermaViz software had a high sensitivity for detection of cutaneous lesions as small as 2mm, which was limited by the quality of the archival photographs. The software registered specific nevi accurately in most cases, with sight errors in a small number of lesions that were primarily located at the edges of the images. In the 17-patient pilot study, registration and alignment of serial images enabled clinicians to identify new and enlarged nevi in 3 to 11 additional patients vs the unregistered images. Quantification with DermaViz correlated with physician assessment of new and enlarged nevi in 90% of evaluated lesions. Conclusion: Software has been designed, applied, and validated that dramatically improves detection of changes in nevi over time and enables quantification of these changes. It helped clinicians to identify numerous changes that were missed in the original unregistered images. We plan to incorporate an expanded ruler and color balance tape in future photographs for improved analyses of border, color, and size changes. Dermaviz will be used in a planned Phase II trial of sulforaphane for therapeutic prevention of melanoma (EA6201). Citation Format: William F. Maguire, Paul H. Haley, Catherine M. Dietz, Mike Hoffelder, Clara S. Brandt, Robin Joyce, Melissa D. Wilson, Darcy Ploucha, Christopher Minnier, Cindy Sander, Hong Wang, Hassane M. Zarour, Kevin J. Mitchell, Ellen K. Hughes, John M. Kirkwood. Automated image registration and alignment facilitates assessment of change in pigmented lesions of patients at risk for melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB227.
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LB227:自动图像配准和对齐有助于评估黑色素瘤风险患者色素病变的变化
简介:黑色素瘤要么从头开始发展,要么从非特异性前体病变发展,称为非典型/发育不良痣。随着时间的推移,评估色素皮肤病变数量和形态的变化对于皮肤癌的早期发现至关重要,并可能为黑色素瘤的早期治疗预防试验提供疗效的初步信号。尽管使用全身数码摄影至少20年来记录这些病变的存在,以及最近在临床图像中计算机辅助诊断病变方面取得的进展,但仍然缺乏表征皮肤病变演变的自动化方法。本研究的目的是开发和验证计算机视觉方法,以方便检测和量化连续数码照片中痣的变化。方法:开发了' DermaViz9数学算法,用于序列图像之间的痣配准和图像对齐,以改善比较。该技术是基于双谱算法,修改以适应人类皮肤的变化。自适应归一化技术调整光照和肤色变化。这些算法的分层迭代加上精确对齐的概率匹配技术,可以适应皮肤的扭曲和剪切。当对齐图像在不同日期之间切换时,该技术既可以提高临床医生的定性比较,也可以对病变的(a)对称性、(b)顺序、(c)颜色和(d)直径的变化进行数字量化。在这项初步研究中,从我们机构已有的图像和痣生物银行协议数据库中获得了17例多发性非典型痣和黑色素瘤病史患者的一系列后截骨照片。去识别图像用DermaViz软件处理和分析,结果由黑色素瘤项目临床医生小组验证。结果:DermaViz软件对小至2mm的皮肤病变的检测灵敏度很高,但受档案照片质量的限制。在大多数情况下,该软件准确地记录了特定的痣,在少数主要位于图像边缘的病变中存在视力错误。在17名患者的初步研究中,序列图像的注册和对齐使临床医生能够在3至11名额外的患者中识别新的和扩大的痣,而不是未注册的图像。用DermaViz量化与医师对90%评估病变的新痣和扩大痣的评估相关。结论:软件的设计、应用和验证显著提高了痣变化的检测,并使这些变化能够量化。它帮助临床医生识别原始未注册图像中遗漏的许多变化。我们计划在未来的照片中加入一个扩展的尺子和色彩平衡带,以改进对边界,颜色和尺寸变化的分析。Dermaviz将用于计划中的萝卜硫素预防黑色素瘤(EA6201)的II期试验。引文格式:William F. Maguire, Paul H. Haley, Catherine M. Dietz, Mike Hoffelder, Clara S. Brandt, Robin Joyce, Melissa D. Wilson, Darcy Ploucha, Christopher Minnier, Cindy Sander, Hong Wang, Hassane M. Zarour, Kevin J. Mitchell, Ellen K. Hughes, John M. Kirkwood。自动图像配准和对齐有助于评估黑色素瘤风险患者色素病变的变化[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志,2021;81(13 -增刊):摘要nr LB227。
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