Skin lesion segmentation using object scale-oriented fully convolutional neural networks
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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.
KeywordsSkin lesion Melanoma Fully convolutional neural networks Object scale-oriented Image segmentation
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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