Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance
| dc.contributor.author | Auger, Daniel A | |
| dc.contributor.author | Zhong, Xiaodong | |
| dc.contributor.author | Epstein, Frederick H | |
| dc.contributor.author | Meintjes, Ernesta M | |
| dc.contributor.author | Spottiswoode, Bruce S | |
| dc.date.accessioned | 2015-07-30T04:09:32Z | |
| dc.date.available | 2015-07-30T04:09:32Z | |
| dc.date.issued | 2014-01-14 | |
| dc.date.updated | 2015-01-15T17:55:46Z | |
| dc.description.abstract | Abstract Background The most time consuming and limiting step in three dimensional (3D) cine displacement encoding with stimulated echoes (DENSE) MR image analysis is the demarcation of the left ventricle (LV) from its surrounding anatomical structures. The aim of this study is to implement a semi-automated segmentation algorithm for 3D cine DENSE CMR using a guide point model approach. Methods A 3D mathematical model is fitted to guide points which were interactively placed along the LV borders at a single time frame. An algorithm is presented to robustly propagate LV epicardial and endocardial surfaces of the model using the displacement information encoded in the phase images of DENSE data. The accuracy, precision and efficiency of the algorithm are tested. Results The model-defined contours show good accuracy when compared to the corresponding manually defined contours as similarity coefficients Dice and Jaccard consist of values above 0.7, while false positive and false negative measures show low percentage values. This is based on a measure of segmentation error on intra- and inter-observer spatial overlap variability. The segmentation algorithm offers a 10-fold reduction in the time required to identify LV epicardial and endocardial borders for a single 3D DENSE data set. Conclusion A semi-automated segmentation method has been developed for 3D cine DENSE CMR. The algorithm allows for contouring of the first cardiac frame where blood-myocardium contrast is almost nonexistent and reduces the time required to segment a 3D DENSE data set significantly. | |
| dc.identifier.apacitation | Auger, D. A., Zhong, X., Epstein, F. H., Meintjes, E. M., & Spottiswoode, B. S. (2014). Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance. <i>Journal of Cardiovascular Magnetic Resonance</i>, http://hdl.handle.net/11427/13621 | en_ZA |
| dc.identifier.chicagocitation | Auger, Daniel A, Xiaodong Zhong, Frederick H Epstein, Ernesta M Meintjes, and Bruce S Spottiswoode "Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance." <i>Journal of Cardiovascular Magnetic Resonance</i> (2014) http://hdl.handle.net/11427/13621 | en_ZA |
| dc.identifier.citation | Auger, D. A., Zhong, X., Epstein, F. H., Meintjes, E. M., & Spottiswoode, B. S. (2014). Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance. J Cardiovasc Magn Reson, 16, 8. | |
| dc.identifier.ris | TY - Journal Article AU - Auger, Daniel A AU - Zhong, Xiaodong AU - Epstein, Frederick H AU - Meintjes, Ernesta M AU - Spottiswoode, Bruce S AB - Abstract Background The most time consuming and limiting step in three dimensional (3D) cine displacement encoding with stimulated echoes (DENSE) MR image analysis is the demarcation of the left ventricle (LV) from its surrounding anatomical structures. The aim of this study is to implement a semi-automated segmentation algorithm for 3D cine DENSE CMR using a guide point model approach. Methods A 3D mathematical model is fitted to guide points which were interactively placed along the LV borders at a single time frame. An algorithm is presented to robustly propagate LV epicardial and endocardial surfaces of the model using the displacement information encoded in the phase images of DENSE data. The accuracy, precision and efficiency of the algorithm are tested. Results The model-defined contours show good accuracy when compared to the corresponding manually defined contours as similarity coefficients Dice and Jaccard consist of values above 0.7, while false positive and false negative measures show low percentage values. This is based on a measure of segmentation error on intra- and inter-observer spatial overlap variability. The segmentation algorithm offers a 10-fold reduction in the time required to identify LV epicardial and endocardial borders for a single 3D DENSE data set. Conclusion A semi-automated segmentation method has been developed for 3D cine DENSE CMR. The algorithm allows for contouring of the first cardiac frame where blood-myocardium contrast is almost nonexistent and reduces the time required to segment a 3D DENSE data set significantly. DA - 2014-01-14 DB - OpenUCT DO - 10.1186/1532-429X-16-8 DP - University of Cape Town J1 - Journal of Cardiovascular Magnetic Resonance LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance TI - Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance UR - http://hdl.handle.net/11427/13621 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/13621 | |
| dc.identifier.uri | http://dx.doi.org/10.1186/1532-429X-16-8 | |
| dc.identifier.vancouvercitation | Auger DA, Zhong X, Epstein FH, Meintjes EM, Spottiswoode BS. Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance. 2014; http://hdl.handle.net/11427/13621. | en_ZA |
| dc.language.rfc3066 | en | |
| dc.publisher.department | MRC/UCT Medical Imaging Research Unit | en_ZA |
| dc.publisher.faculty | Faculty of Health Sciences | en_ZA |
| dc.publisher.institution | University of Cape Town | |
| dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution License | * |
| dc.rights.holder | Auger et al.; licensee BioMed Central Ltd. | |
| dc.rights.uri | http://creativecommons.org/licenses/by/2.0 | * |
| dc.source | Journal of Cardiovascular Magnetic Resonance | en_ZA |
| dc.source.uri | http://www.jcmr-online.com | |
| dc.subject.other | Cardiovascular MR | en_ZA |
| dc.subject.other | DENSE | en_ZA |
| dc.subject.other | Segmentation | en_ZA |
| dc.subject.other | Guide point modeling | en_ZA |
| dc.title | Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance | |
| dc.type | Journal Article | en_ZA |
| uct.type.filetype | ||
| uct.type.filetype | Text | |
| uct.type.filetype | Image | |
| uct.type.publication | Research | en_ZA |
| uct.type.resource | Article | en_ZA |