Skin lesion boundary segmentation with fully automated deep extreme cut methods

Manu Goyal; Jiahua Ng; Amanda Oakley; Moi Hoon Yap

doi:10.1117/12.2513015

15 March 2019 Skin lesion boundary segmentation with fully automated deep extreme cut methods

Manu Goyal, Jiahua Ng, Amanda Oakley, Moi Hoon Yap

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109530Q (2019) https://doi.org/10.1117/12.2513015
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Manu Goyal, Jiahua Ng, Amanda Oakley, and Moi Hoon Yap "Skin lesion boundary segmentation with fully automated deep extreme cut methods", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530Q (15 March 2019); https://doi.org/10.1117/12.2513015

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