Dataset Information

Automated delineation of dermal-epidermal junction in reflectance confocal microscopy image stacks of human skin.

ABSTRACT: Reflectance confocal microscopy (RCM) images skin noninvasively, with optical sectioning and nuclear-level resolution comparable with that of pathology. On the basis of the assessment of the dermal-epidermal junction (DEJ) and morphologic features in its vicinity, skin cancer can be diagnosed in vivo with high sensitivity and specificity. However, the current visual, qualitative approach for reading images leads to subjective variability in diagnosis. We hypothesize that machine learning-based algorithms may enable a more quantitative, objective approach. Testing and validation were performed with two algorithms that can automatically delineate the DEJ in RCM stacks of normal human skin. The test set was composed of 15 fair- and 15 dark-skin stacks (30 subjects) with expert labelings. In dark skin, in which the contrast is high owing to melanin, the algorithm produced an average error of 7.9±6.4??m. In fair skin, the algorithm delineated the DEJ as a transition zone, with average error of 8.3±5.8??m for the epidermis-to-transition zone boundary and 7.6±5.6??m for the transition zone-to-dermis. Our results suggest that automated algorithms may quantitatively guide the delineation of the DEJ, to assist in objective reading of RCM images. Further development of such algorithms may guide assessment of abnormal morphological features at the DEJ.

SUBMITTER: Kurugol S

PROVIDER: S-EPMC4323765 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Automated delineation of dermal-epidermal junction in reflectance confocal microscopy image stacks of human skin.

Kurugol Sila S Kose Kivanc K Park Brian B Dy Jennifer G JG Brooks Dana H DH Rajadhyaksha Milind M

The Journal of investigative dermatology 20140903 3

Reflectance confocal microscopy (RCM) images skin noninvasively, with optical sectioning and nuclear-level resolution comparable with that of pathology. On the basis of the assessment of the dermal-epidermal junction (DEJ) and morphologic features in its vicinity, skin cancer can be diagnosed in vivo with high sensitivity and specificity. However, the current visual, qualitative approach for reading images leads to subjective variability in diagnosis. We hypothesize that machine learning-based a ...[more]

PMID: 25184959

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Project description:BACKGROUND:Phosphorylated histone H2AX, also known as ?H2AX, forms ?m-sized nuclear foci at the sites of DNA double-strand breaks (DSBs) induced by ionizing radiation and other agents. Due to their specificity and sensitivity, ?H2AX immunoassays have become the gold standard for studying DSB induction and repair. One of these assays relies on the immunofluorescent staining of ?H2AX followed by microscopic imaging and foci counting. During the last years, semi- and fully automated image analysis, capable of fast detection and quantification of ?H2AX foci in large datasets of fluorescence images, are gradually replacing the traditional method of manual foci counting. A major drawback of the non-commercial software for foci counting (available so far) is that they are restricted to 2D-image data. In practice, these algorithms are useful for counting the foci located close to the midsection plane of the nucleus, while the out-of-plane foci are neglected. RESULTS:To overcome the limitations of 2D foci counting, we present a freely available ImageJ-based plugin (FocAn) for automated 3D analysis of ?H2AX foci in z-image stacks acquired by confocal fluorescence microscopy. The image-stack processing algorithm implemented in FocAn is capable of automatic 3D recognition of individual cell nuclei and ?H2AX foci, as well as evaluation of the total foci number per cell nucleus. The FocAn algorithm consists of two parts: nucleus identification and foci detection, each employing specific sequences of auto local thresholding in combination with watershed segmentation techniques. We validated the FocAn algorithm using fluorescence-labeled ?H2AX in two glioblastoma cell lines, irradiated with 2?Gy and given up to 24?h post-irradiation for repair. We found that the data obtained with FocAn agreed well with those obtained with an already available software (FoCo) and manual counting. Moreover, FocAn was capable of identifying overlapping foci in 3D space, which ensured accurate foci counting even at high DSB density of up to ~?200 DSB/nucleus. CONCLUSIONS:FocAn is freely available an open-source 3D foci analyzer. The user-friendly algorithm FocAn requires little supervision and can automatically count the amount of DNA-DSBs, i.e. fluorescence-labeled ?H2AX foci, in 3D image stacks acquired by laser-scanning microscopes without additional nuclei staining.

Project description:BACKGROUND:The surgical removal of non-melanoma skin cancers (NMSCs) is guided by the pathologic examination of margins. However, the preparation of histopathology is time consuming, labour-intensive and requires separate laboratory infrastructure. Furthermore, when histopathology indicates positive margins, patients must return for re-excisions. Reflectance confocal microscopy (RCM) with a new video-mosaicking approach can noninvasively delineate margins directly on patients and potentially guide surgery in real-time, augmenting the traditional approaches of histopathology. OBJECTIVE:To assess a new peri-operative RCM video-mosaicking approach for comprehensive delineation of NMSC margins on patients in vivo. METHODS:Thirty-five patients undergoing Mohs micrographic surgery (MMS) in the Mohs surgery unit at Memorial Sloan Kettering Cancer Center, New York, NY were included in the study. RCM imaging was performed before and after the first staged excision by acquiring videos along the surgical margins (epidermal, peripheral and deep dermal) of each wound, which were subsequently processed into video-mosaics. Two RCM evaluators read and assessed video-mosaics, and subsequently compared to the corresponding Mohs frozen histopathology. RESULTS:Reflectance confocal microscopy videos and video-mosaics displayed acceptable imaging quality (resolution and contrast), pre-operatively in 32/35 (91%) NMSC lesions and intra-operatively in 29/35 lesions (83%). Pre-operative delineation of margins correlated with the histopathology in 32/35 (91%) lesions. Intra-operative delineation correlated in 10/14 (71%) lesions for the presence of residual tumour and in 18/21 (86%) lesions for absence. Sensitivity/specificity were 71%/86% and 86%/81% for two RCM video-mosaic evaluators, and overall agreement was 80% and 83% with histopathology, with moderate inter-evaluator agreement (k = 0.59, P ≤ 0.0002). CONCLUSIONS:Peri-operative RCM video-mosaicking of NMSC margins directly on patients may potentially guide surgery in real-time, serve as an adjunct to histopathology, reduce time spent in clinic and reduce the need for re-excisions. Further testing in larger studies is needed.

Dataset Information

Automated delineation of dermal-epidermal junction in reflectance confocal microscopy image stacks of human skin.

Publications

Automated delineation of dermal-epidermal junction in reflectance confocal microscopy image stacks of human skin.

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