Abstract
Background Atypical vascular pattern is one of the most important features by differentiating between benign and malignant pigmented skin lesions. Detection and analysis of vascular structures is a necessary initial step for skin mole assessment; it is a prerequisite step to provide an accurate outcome for the widely used 7-point checklist diagnostic algorithm. Methods In this research we present a fully automated machine learning approach for segmenting vascular structures in dermoscopy colour images. The U-Net architecture is based on convolutional networks and designed for fast and precise segmentation of images. After preprocessing the images are randomly divided into 146516 patches of 64 × 64 pixels each. Results On the independent validation dataset including 74 images our implemented method showed high segmentation accuracy. For the U-Net convolutional neural network, an average DSC of 0.84, sensitivity 0.85, and specificity 0.81 has been achieved. Conclusion Vascular structures due to small size and similarity to other local structures create enormous difficulties during the segmentation and assessment process. The use of advanced segmentation methods like deep learning, especially convolutional neural networks, has the potential to improve the accuracy of advanced local structure detection.
Highlights
Melanoma is the deadliest form of skin cancer which develops when skin cells multiply rapidly as a consequence of mutations in their DNA caused by the sun’s ultraviolet (UV) radiation (Figure 1)
For 2018 it is predicted that 14,320 new cases of melanoma skin cancer will be diagnosed in Australia which is estimated to be 10,4% of all new diagnosed cancer cases [3]
In this research we have obtained accurate and comprehensive results showing that the applying of U-Net neural networks for local structure detection in dermoscopy colour images brings a valuable alternative to vascular structure detection
Summary
Melanoma is the deadliest form of skin cancer which develops when skin cells multiply rapidly as a consequence of mutations in their DNA caused by the sun’s ultraviolet (UV) radiation (Figure 1). Melanoma is a cancer that starts in the melanocytes which are cells that make a brown pigment called melanin, which gives the skin its tan or brown colour [1]. Other names for this cancer include malignant melanoma and cutaneous melanoma. In this research we present a fully automated machine learning approach for segmenting vascular structures in dermoscopy colour images. On the independent validation dataset including 74 images our implemented method showed high segmentation accuracy. The use of advanced segmentation methods like deep learning, especially convolutional neural networks, has the potential to improve the accuracy of advanced local structure detection
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