Multi-atlas segmentation using manifold learning with deep belief networks

Simple item page
dc.contributor.authorNascimento, J.
dc.contributor.authorCarneiro, G.
dc.contributor.conference2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016) (13 Apr 2016 - 16 Apr 2016 : Prague, Czech Republic)
dc.date.issued2016
dc.description.abstractThis paper proposes a novel combination of manifold learning with deep belief networks for the detection and segmentation of left ventricle (LV) in 2D - ultrasound (US) images. The main goal is to reduce both training and inference complexities while maintaining the segmentation accuracy of machine learning based methods for non-rigid segmentation methodologies. The manifold learning approach used can be viewed as an atlas-based segmentation. It partitions the data into several patches. Each patch proposes a segmentation of the LV that somehow must be fused. This is accomplished by a deep belief network (DBN) multi-classifier that assigns a weight for each patch LV segmentation. The approach is thus threefold: (i) it does not rely on a single segmentation, (ii) it provides a great reduction in the rigid detection phase that is performed at lower dimensional space comparing with the initial contour space, and (iii) DBN's allows for a training process that can produce robust appearance models without the need of large annotated training sets.
dc.description.statementofresponsibilityJacinto C. Nascimento, Gustavo Carneiro
dc.identifier.citationProceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2016, vol.2016-June, pp.867-871
dc.identifier.doi10.1109/ISBI.2016.7493403
dc.identifier.isbn9781479923502
dc.identifier.issn1945-7928
dc.identifier.issn1945-8452
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]
dc.identifier.urihttp://hdl.handle.net/2440/107659
dc.language.isoen
dc.publisherIEEE
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140102794
dc.relation.ispartofseriesIEEE International Symposium on Biomedical Imaging
dc.rights© 2016 IEEE
dc.source.urihttps://doi.org/10.1109/isbi.2016.7493403
dc.subjectManifolds, training, image segmentation, visualization, principal component analysis, complexity theory, context
dc.titleMulti-atlas segmentation using manifold learning with deep belief networks
dc.typeConference paper
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
RA_hdl_107659.pdf
Size:
797.48 KB
Format:
Adobe Portable Document Format
Description:
Restricted Access
Download

Collections

Computer Science publications