A Deep Learning Approach to Vascular Structure Segmentation in Dermoscopy Colour Images.
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 out...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Formato: | research Journal Article |
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Wiley-Blackwell
11/1/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132770521&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132770521 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/1/2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 132770521 132770521 132770521 10.1155/2018/5049390 132770521 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Deep Learning Approach to Vascular Structure Segmentation in Dermoscopy Colour Images. aug: au: Jaworek-Korjakowska, Joanna affil: Department of Automatic Control and Robotics, AGH University of Science and Technology, Cracow, Poland sug: subj: Melanoma Diagnosis Microscopy Methods Image Processing, Computer Assisted Utilization Human Skin Neoplasms Classification Neural Networks (Computer) Methods Sensitivity and Specificity Artificial Intelligence Melanoma Prevention and Control Melanoma Surgery ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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