In a double blind evaluation of 60 digital dermatoscopic images by 4 "junior", 4 "senior" and 4 "expert" dermatologists (dermatoscopy training respectively less than 1 year, between 1 and 5 years, and more than 5 years), a significant inter-operator variability was observed in melanocytic lesion border identification (with a disagreement of the order of 10 - 20% of the area of the lesions). Expert dermatologists showed greater agreement among themselves than with senior and junior dermatologists, and a slight tendency towards "tighter" segmentations. The human inter-operator variability was then used to evaluate the segmentation accuracy of 4 algorithms, representative of the 3 fundamental state-of-the-art automated segmentation techniques and of a fourth, novel, technique. Our evaluation methodology addresses a number of crucial difficulties encountered in previous studies and may be of independent interest. 3 of the 4 algorithms showed considerably less agreement with expert dermatologists than even senior and junior dermatologists did (with a disagreement of the order of 30% of the area of the lesions); the remaining algorithm, however, showed agreement with expert dermatologists comparable to that of other expert dermatologists.
Variability in human and automatic localization of melanocytic lesions
SILLETTI, ALBERTO;PESERICO STECCHINI NEGRI DE SALVI, ENOCH;ZATTRA, EDOARDO;PESERICO STECCHINI NEGRI DE SALVI, ANDREA;BELLONI, FORTINA ANNA
2009
Abstract
In a double blind evaluation of 60 digital dermatoscopic images by 4 "junior", 4 "senior" and 4 "expert" dermatologists (dermatoscopy training respectively less than 1 year, between 1 and 5 years, and more than 5 years), a significant inter-operator variability was observed in melanocytic lesion border identification (with a disagreement of the order of 10 - 20% of the area of the lesions). Expert dermatologists showed greater agreement among themselves than with senior and junior dermatologists, and a slight tendency towards "tighter" segmentations. The human inter-operator variability was then used to evaluate the segmentation accuracy of 4 algorithms, representative of the 3 fundamental state-of-the-art automated segmentation techniques and of a fourth, novel, technique. Our evaluation methodology addresses a number of crucial difficulties encountered in previous studies and may be of independent interest. 3 of the 4 algorithms showed considerably less agreement with expert dermatologists than even senior and junior dermatologists did (with a disagreement of the order of 30% of the area of the lesions); the remaining algorithm, however, showed agreement with expert dermatologists comparable to that of other expert dermatologists.Pubblicazioni consigliate
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