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Skin lesion boundary segmentation with fully automated deep extreme cut methods
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Paper Abstract

The skin is the largest organ in our body. There is a high prevalence of skin diseases and a scarcity of dermatologists, the experts in diagnosing and managing skin diseases, making CAD (Computer Aided Diagnosis) of skin disease an important field of research. Many patients present with a skin lesion of concern, to determine if it is benign or malignant. Lesion diagnosis is currently performed by dermatologists taking a history and examining the lesion and the entire body surface with the aid of a dermatoscope. Automatic lesion segmentation and evaluation of the symmetry or asymmetry of structures and colors with the help of computers may classify a lesion as likely benign or as likely malignant. We have explored a deep learning program called Deep Extreme Cut (DEXTR) and used the Faster-RCNN-InceptionV2 network to determine extreme points (left-most, right-most, top and bottom pixels). We used the ISIC challenge-2017 images for the training set and received Jaccard index of 82.2% on the ISIC testing set 2017 and 85.8% on the PH2 dataset. The proposed method outperformed the winner algorithm of the competition by 5.7% for the Jaccard index.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530Q (15 March 2019); doi: 10.1117/12.2513015
Show Author Affiliations
Manu Goyal, Manchester Metropolitan Univ. (United Kingdom)
Jiahua Ng, The Univ. of Sheffield (United Kingdom)
Amanda Oakley, DermNet (New Zealand)
Moi Hoon Yap, Manchester Metropolitan Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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