ArticleBioMed research international2018
A Deep Learning Approach to Vascular Structure Segmentation in Dermoscopy Colour Images.
Article in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Development of an Automated Morphometric Approach to Assess Vascular Outcomes following Exposure to Environmental Chemicals in Zebrafish.Environmental health perspectives · 2024Article
- Dermoscopic Features of Cutaneous Vasculitis.Dermatology practical & conceptual · 2024Article
- A three-dimensional method for morphological analysis and flow velocity estimation in microvasculature on-a-chip.Bioengineering & translational medicine · 2023Article
- Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study.JMIR medical informatics · 2023Article
- Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.Skeletal radiology · 2022Article
- Methods and Limits for Micro Scale Blood Vessel Flow Imaging in Scattering Media by Optical Feedback Interferometry: Application to Human Skin.Sensors (Basel, Switzerland) · 2021Article
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Abstract
backgroundAtypical 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.
methodsIn 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.
resultsOn 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.
conclusionVascular 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.
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