Search: onr:"swepub:oai:DiVA.org:kth-212015" >
Classification of c...
Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
-
- Lidayová, Kristína (author)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
-
Gupta, Anindya (author)
-
- Frimmel, Hans (author)
- Uppsala universitet,Avdelningen för beräkningsvetenskap
-
show more...
-
- Sintorn, Ida-Maria (author)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
-
- Bengtsson, Ewert (author)
- Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion
-
- Smedby, Örjan (author)
- KTH,Medicinsk bildbehandling och visualisering
-
show less...
-
(creator_code:org_t)
- 2017-06-22
- 2017
- English.
-
In: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017. - Cham : Springer. - 9783319609638 ; , s. 182-194
- Related links:
-
https://urn.kb.se/re...
-
show more...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
show less...
Abstract
Subject headings
Close
- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- Classification
- Convolutional neural networks
- CT angiography
- Vascular skeleton
- Computerized Image Processing
Publication and Content Type
- ref (subject category)
- kon (subject category)
Find in a library
To the university's database