Automatic Aortic Wall Segmentation and Plaque Detection using Deep Convolutional Neural Networks
| dc.contributor.author | Beetz, Marcel | |
| dc.contributor.editor | Becker, Michael | |
| dc.date.accessioned | 2019-10-14T11:50:20Z | |
| dc.date.available | 2019-10-14T11:50:20Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Abnormal aortic wall thickness and the presence of aortic plaque have been linked to various types of cardiovascular disease. QuantiĄcation of both indicators currently depends on manual or semi-automatic methods which suffer from limited quality and long acquisition times. This work presents various fully automatic state-of-the art solutions to two medical image processing problems: aortic wall segmentation and plaque slice detection. A u-net derived residual convolutional neural network (CNN), a cascaded pipeline of two CNNs and a 3D CNN architecture are used for aortic wall segmentation. Plaque detection is performed by a standard multilayer residual CNN classification architecture, a u-net derived CNN classifier and a capsule CNN. The experiments show that the u-net inspired residual CNN performs best at the aortic wall segmentation task with a Dice score of around 0.8 while the capsule CNN achieves the best results in slice-wise plaque detection with a precision of 0.74 and an accuracy of 0.68. | en |
| dc.identifier.isbn | 978-3-88579-448-6 | |
| dc.identifier.pissn | 1614-3213 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/28976 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | SKILL 2018 - Studierendenkonferenz Informatik | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Seminars, Volume S-14 | |
| dc.subject | Deep Learning | |
| dc.subject | Convolutional Neural Networks | |
| dc.subject | Capsule Networks | |
| dc.subject | Aortic Wall Segmentation | |
| dc.subject | Plaque Detection | |
| dc.subject | Medical Image Processing | |
| dc.title | Automatic Aortic Wall Segmentation and Plaque Detection using Deep Convolutional Neural Networks | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 168 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 157 | |
| gi.conference.date | 26.-27. September 2018 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | Neuronale Netze |
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