sections_250

This file contains the complete dataset of en-face sections at a resolution of 250x250 pixels. This file contains one dataset called 'sections' - this is a three dimensional array of uint8 values. The first axis is individual sections in the dataset. The second and third axes are the rows and columns of intensity values of that section

Metadata for all sections in dataset

Identifies the characteristics of each RCM en-face section in the dataset. A detailed description is included in the README.txt file

Percentage of correctly classified sections by the automated approach in each subgroup.

<p>Percentage of correctly classified sections by the automated approach in each subgroup.</p

Comparison of human intraobserver agreement with automated approach on the 5319 sections in the test set.

<p>Comparison of human intraobserver agreement with automated approach on the 5319 sections in the test set.</p

Accuracy and agreement of the automated approach with the dermatologist for individual stacks.

<p>A) Classification accuracy across all test stacks, organised by participant. The annotated examples are the best, average and worst accuracy stacks shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153208#pone.0153208.g003" target="_blank">Fig 3</a>. 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.</p

Overview of analysis steps in automated actinic keratosis detection, as applied to the dorsum of hand with the contrast adjusted for visualization.

<p>A) Input image. B) YCbCr transform of input image. C) Mean of Cb and Cr channels shows distinct hotspots for erythema. D) Guided filtering to remove unneeded texture. E) Distinct peaks extracted by morphological analysis. F) Hysteresis thresholding to identify erythematic areas. G) Boundaries of automatically detected lesions (white) compared with the dermatologist’s annotations (blue). </p

Comparison of automatically detected lesions with dermatologist circumscribed lesions for each body site.

<p>Each point represents the total count for each method from one clinical photograph in the severe photodamage group. Labelled points correspond to photographs shown. White outlines are automatically detected regions, blue/green outlines are dermatologist annotations. A) Automatically counted lesions on each face photograph compared with dermatologist count, and number of co-localized lesions. B) Dermatologist second count on faces compared with first count, and number of co-localized lesions. C-D) Example of automated output compared with dermatologist on two foreheads. E) Automatically counted lesions on each arm photograph compared with dermatologist count, and number of co-localized lesions. F) Dermatologist second count on arms compared with first count, and number of co-localized lesions. G-H) Example of automated output compared with dermatologist annotation on forearm and hand. </p

Impact of different parameters on automatically identified actinic keratosis lesions.

<p>A-C) The texture regularization parameter ϵ controls how smooth the detected lesions boundaries are. D-F) The radius of the disc used in morphological opening by reconstruction controls the size of detected lesions. G-I) The high hysteresis threshold controls whether or not a potential lesion is included based on the maximum erythema intensity. </p

Confusion between automated/dermatologist assessment and the first and second dermatologist assessments on the face and arms.

<p>Confusion between automated/dermatologist assessment and the first and second dermatologist assessments on the face and arms.</p

Values of integrated density and positive area in epidermis and dermis at 1 hour and 24 hour time points.

<p>* indicates p<0.05 and ** indicates p<0.01.</p