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Automated Segmentation of Skin Strata in Reflectance Confocal Microscopy Depth Stacks.

Hames SC, Ardigò M, Soyer HP, Bradley AP, Prow TW - PLoS ONE (2016)

Bottom Line: This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20-30 and 50-70 years of age).The classification accuracy on the test set was 85.6%.The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively.

View Article: PubMed Central - PubMed

Affiliation: Dermatology Research Centre, The University of Queensland, School of Medicine, Translational Research Institute, Brisbane, Australia.

ABSTRACT
Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20-30 and 50-70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively. The probabilities predicted by the classifier in the test set showed that the classifier learned an effective model of the anatomy of human skin.

No MeSH data available.


Related in: MedlinePlus

Accuracy and agreement of the automated approach with the dermatologist for individual stacks.A) Classification accuracy across all test stacks, organised by participant. The annotated examples are the best, average and worst accuracy stacks shown in Fig 3. B-D) The correlation between the dermatologist identified interface and the automatically identified interface for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces.
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pone.0153208.g002: Accuracy and agreement of the automated approach with the dermatologist for individual stacks.A) Classification accuracy across all test stacks, organised by participant. The annotated examples are the best, average and worst accuracy stacks shown in Fig 3. B-D) The correlation between the dermatologist identified interface and the automatically identified interface for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces.

Mentions: The classification accuracy for each stack, and the agreement between the automatic and dermatologist identified interfaces between strata are shown in Fig 2. The average absolute error and standard deviation in locating all interfaces was 4.8 ± 4.8 μm The average absolute error and standard deviation of the error in locating each interface was 3.1 ± 3.3 μm, 6.0 ± 5.3 μm and 5.5 ± 5.0 μm for the interfaces between the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis respectively. The complete classification results for each stack in the test set are given in S2 Table.


Automated Segmentation of Skin Strata in Reflectance Confocal Microscopy Depth Stacks.

Hames SC, Ardigò M, Soyer HP, Bradley AP, Prow TW - PLoS ONE (2016)

Accuracy and agreement of the automated approach with the dermatologist for individual stacks.A) Classification accuracy across all test stacks, organised by participant. The annotated examples are the best, average and worst accuracy stacks shown in Fig 3. B-D) The correlation between the dermatologist identified interface and the automatically identified interface for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835045&req=5

pone.0153208.g002: Accuracy and agreement of the automated approach with the dermatologist for individual stacks.A) Classification accuracy across all test stacks, organised by participant. The annotated examples are the best, average and worst accuracy stacks shown in Fig 3. B-D) The correlation between the dermatologist identified interface and the automatically identified interface for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces.
Mentions: The classification accuracy for each stack, and the agreement between the automatic and dermatologist identified interfaces between strata are shown in Fig 2. The average absolute error and standard deviation in locating all interfaces was 4.8 ± 4.8 μm The average absolute error and standard deviation of the error in locating each interface was 3.1 ± 3.3 μm, 6.0 ± 5.3 μm and 5.5 ± 5.0 μm for the interfaces between the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis respectively. The complete classification results for each stack in the test set are given in S2 Table.

Bottom Line: This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20-30 and 50-70 years of age).The classification accuracy on the test set was 85.6%.The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively.

View Article: PubMed Central - PubMed

Affiliation: Dermatology Research Centre, The University of Queensland, School of Medicine, Translational Research Institute, Brisbane, Australia.

ABSTRACT
Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20-30 and 50-70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively. The probabilities predicted by the classifier in the test set showed that the classifier learned an effective model of the anatomy of human skin.

No MeSH data available.


Related in: MedlinePlus