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Skin lesion segmentation using object scale-oriented fully convolutional neural networks

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Abstract

Melanoma is the deadliest form of skin cancer, and its incidence level is increasing. It is important to obtain a diagnosis at an early stage to increase the patient survival rate. Skin lesion segmentation is a difficult problem in medical image analysis. To address this problem, we propose end-to-end object scale-oriented fully convolutional networks (OSO–FCNs) for skin lesion segmentation. Given a single skin lesion image, the proposed method produces a pixel-level mask for skin lesion areas. We found that the scale of the lesions in the training dataset affects a large number of the segmentation results of the lesions in the testing phase, and thus, a training strategy called object scale-oriented (OSO) training is proposed. First, the pre-trained network of VGG-16 is adapted and is transformed into fully convolutional networks (FCNs). Second, after very simple preprocessing, skin lesion images with boundary-level annotations are fed into the FCNs for fine-tuning training based on the pre-trained model using OSO training. During the OSO training, the training dataset is divided into 2 subsets according to an index called the object occupation ratio, and then the whole training dataset and the 2 subsets are used to train 3 different scale-oriented FCNs. A dataset provided by the International Skin Imaging Collaboration (ISIC), ISIC2016, is used for training and testing. Our algorithm is compared with the state-of-the-art algorithms, and the experimental results demonstrate that the segmentation accuracy of our algorithm is higher or very close to the performances of the other algorithms.

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Acknowledgements

This research was partly supported by the Guangxi Natural Science Foundation (2018JJB170004), the Guangxi Basic Ability Promotion Project for Young and Middle-aged Teachers (2017KY0247), the Project of Cultivating a Thousand Young and Middle-aged Teachers in Guangxi Universities, the Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System (2018A-07), and the Guangxi Universities Key Laboratory Fund of Embedded Technology and Intelligent Information Processing (2017-1-1, 2017-2-4). Additionally, we would like to thank NVIDIA for providing the Titan X GPU used in this research.

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Authors and Affiliations

  1. Xidian University, Xi’an, Shanxi, China

    Lin Huang & Yi-gong Zhao

  2. Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, Guangxi, China

    Lin Huang & Tie-jun Yang

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  1. Lin Huang
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  2. Yi-gong Zhao
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Correspondence to Yi-gong Zhao.

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Huang, L., Zhao, Yg. & Yang, Tj. Skin lesion segmentation using object scale-oriented fully convolutional neural networks. SIViP 13, 431–438 (2019). https://doi.org/10.1007/s11760-018-01410-3

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