dc.contributor.author | Al Arif, SMMR | |
dc.contributor.author | Knapp, K | |
dc.contributor.author | Slabaugh, G | |
dc.date.accessioned | 2019-11-26T12:24:16Z | |
dc.date.issued | 2018-09-26 | |
dc.description.abstract | Shape has widely been used in medical image segmentation algorithms to constrain a segmented region to a class of learned shapes. Recent methods for object segmentation mostly use deep learning algorithms. The state-of-the-art deep segmentation networks are trained with loss functions defined in a pixel-wise manner, which is not suitable for learning topological shape information and constraining segmentation results. In this paper, we propose a novel shape predictor network for object segmentation. The proposed deep fully convolutional neural network learns to predict shapes instead of learning pixel-wise classification. We apply the novel shape predictor network to X-ray images of cervical vertebra where shape is of utmost importance. The proposed network is trained with a novel loss function that computes the error in the shape domain. Experimental results demonstrate the effectiveness of the proposed method to achieve state-of-the-art segmentation, with correct topology and accurate fitting that matches expert segmentation. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | Royal Devon and Exeter NHS Foundation Trust | en_GB |
dc.identifier.citation | In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, edited by A. Frangi, J. Schnabel, C. Davatzikos, C. Alberola-López, and G. Fichtinger, pp. 430-439 . Lecture Notes in Computer Science, vol 11070 | en_GB |
dc.identifier.doi | 10.1007/978-3-030-00928-1_49 | |
dc.identifier.uri | http://hdl.handle.net/10871/39804 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | © Springer Nature Switzerland AG 2018 | en_GB |
dc.title | SPNet: Shape Prediction Using a Fully Convolutional Neural Network | en_GB |
dc.type | Book chapter | en_GB |
dc.date.available | 2019-11-26T12:24:16Z | |
dc.relation.isPartOf | Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 | en_GB |
dc.description | This is the author accepted manuscript. The final version is available from Springer via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Royal Devon & Exeter NHS Foundation Trst | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2018-09-26 | |
rioxxterms.type | Book chapter | en_GB |
refterms.dateFCD | 2019-11-26T12:22:56Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-11-26T12:24:20Z | |