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Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
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2017 (Engelska)Ingår i: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, Springer, 2017, Vol. 723, s. 182-194Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.

Ort, förlag, år, upplaga, sidor
Springer, 2017. Vol. 723, s. 182-194
Serie
Communications in Computer and Information Science, ISSN 1865-0929 ; 723
Nyckelord [en]
Classification, Convolutional neural networks, CT angiography, Vascular skeleton
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
URN: urn:nbn:se:kth:diva-212015DOI: 10.1007/978-3-319-60964-5_16Scopus ID: 2-s2.0-85022182486ISBN: 9783319609638 (tryckt)OAI: oai:DiVA.org:kth-212015DiVA, id: diva2:1133363
Konferens
21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, Edinburgh, United Kingdom, 11 July 2017 through 13 July 2017
Forskningsfinansiär
Vetenskapsrådet, 621-2014-6153
Anmärkning

QC 20170815

Tillgänglig från: 2017-08-15 Skapad: 2017-08-15 Senast uppdaterad: 2017-12-07Bibliografiskt granskad

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Totalt: 185 träffar
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