TY - JOUR
T1 - Automated analysis of scattering-based light sheet microscopy images of anal squamous intraepithelial lesions
AU - Kim, Yongjun
AU - Zhao, Jingwei
AU - Liang, Brooke
AU - Sugimura, Momoka
AU - Marcelino, Kenneth
AU - Romero, Rafael
AU - Nessaee, Ameer
AU - Ocaya, Carmella
AU - Lim, Koeun
AU - Roe, Denise
AU - Khan, Michelle J.
AU - Yang, Eric J.
AU - Kang, Dongkyun
N1 - Publisher Copyright:
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
PY - 2024/9/1
Y1 - 2024/9/1
N2 - We developed an algorithm for automatically analyzing scattering-based light sheet microscopy (sLSM) images of anal squamous intraepithelial lesions. We developed a method for automatically segmenting sLSM images for nuclei and calculating seven features: nuclear intensity, intensity slope as a function of depth, nuclear-to-nuclear distance, nuclear-to-cytoplasm ratio, cell density, nuclear area, and proportion of pixels corresponding to nuclei. 187 images from 80 anal biopsies were used for feature analysis and classifier development. The automated nuclear segmentation method provided reliable performance with the precision of 0.97 and recall of 0.91 when compared with the manual segmentation. Among the seven features, six showed statistically significant differences between high-grade squamous intraepithelial lesion (HSIL) and non-HSIL (non-dysplastic or low-grade squamous intraepithelial lesion, LSIL). A classifier using linear support vector machine (SVM) achieved promising performance in diagnosing HSIL versus non-HSIL: sensitivity of 90%, specificity of 70%, and area under the curve (AUC) of 0.89 for per-image diagnosis, and sensitivity of 90%, specificity of 80%, and AUC of 0.92 for per-biopsy diagnosis.
AB - We developed an algorithm for automatically analyzing scattering-based light sheet microscopy (sLSM) images of anal squamous intraepithelial lesions. We developed a method for automatically segmenting sLSM images for nuclei and calculating seven features: nuclear intensity, intensity slope as a function of depth, nuclear-to-nuclear distance, nuclear-to-cytoplasm ratio, cell density, nuclear area, and proportion of pixels corresponding to nuclei. 187 images from 80 anal biopsies were used for feature analysis and classifier development. The automated nuclear segmentation method provided reliable performance with the precision of 0.97 and recall of 0.91 when compared with the manual segmentation. Among the seven features, six showed statistically significant differences between high-grade squamous intraepithelial lesion (HSIL) and non-HSIL (non-dysplastic or low-grade squamous intraepithelial lesion, LSIL). A classifier using linear support vector machine (SVM) achieved promising performance in diagnosing HSIL versus non-HSIL: sensitivity of 90%, specificity of 70%, and area under the curve (AUC) of 0.89 for per-image diagnosis, and sensitivity of 90%, specificity of 80%, and AUC of 0.92 for per-biopsy diagnosis.
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U2 - 10.1364/BOE.531700
DO - 10.1364/BOE.531700
M3 - Article
AN - SCOPUS:85203179529
SN - 2156-7085
VL - 15
SP - 5547
EP - 5559
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 9
ER -