A Deep Learning Approach to Vascular Structure Segmentation in Dermoscopy Colour Images

By Joanna Jaworek-Korjakowska. 2018

17 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Oncology, Oral Surgery

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Citations in the corpus

Style transfer-based image synthesis as an efficient regularization technique in deep learning (2019, methodology): “As a case study, we choose very challenging benchmark dataset with skin lesions used for classification and segmentation tasks [12].”