dc.creator | Valença, Janaina Bussola Montrezor [UNIFESP] | |
dc.creator | Ferraz, Karoline Pereira [UNIFESP] | |
dc.creator | Alencar, Maria do Carmo Baracho de [UNIFESP] | |
dc.creator | Souza, Felipe Granado [UNIFESP] | |
dc.creator | Lopes, Lucy Vitale | |
dc.date.accessioned | 2019-01-21T10:29:29Z | |
dc.date.accessioned | 2022-10-07T20:58:00Z | |
dc.date.available | 2019-01-21T10:29:29Z | |
dc.date.available | 2022-10-07T20:58:00Z | |
dc.date.created | 2019-01-21T10:29:29Z | |
dc.date.issued | 2016 | |
dc.identifier | 2016 International Joint Conference On Neural Networks (IJCNN). New york, p. 5072-5078, 2016. | |
dc.identifier | 2161-4393 | |
dc.identifier | http://repositorio.unifesp.br/handle/11600/49242 | |
dc.identifier | 10.1109/IJCNN.2016.7727868 | |
dc.identifier | WOS:000399925505038 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/4025446 | |
dc.description.abstract | Echocardiographic exams allow the observation and extraction of measures related to cardiac structures. In the longitudinal parasternal view, these measures include the left ventricle end-diastolic and end-systolic diameters, end-diastolic interventricular septum thickness (IVSd), and end-diastolic left ventricle posterior wall thickness (LVPWd). Among these measures, the IVSd is important for diagnosing pathologies like hypertrophic cardiomyopathy, aneurysms, abnormal movement and structural faults. This work presents a hybrid neural network system to segment interventricular septum in echocardiographic images of parasternal longitudinal view. The hybrid system developed here consist of a Self-Organizing Map and a Multilayer Perceptron (MLP) neural network. The approach has two phases: clustering and classification. First, the Self-Organizing Map clusters image patches that are previously labeled as Septum and Non-septum. Later, an MLP is trained with information generated by the map. The MLP is then employed to classify patches of a new image resulting in a mask that indicates the probable septum regions. To validate the results, we did a semi-automatic extraction of septum thickness. The average error between the septum thicknesses obtained by the algorithm and the one manually traced was 0.5477mm +/- 0.5277mm. Future recommendations are presented to improve the hybrid system performance to get more accurate results. | |
dc.language | eng | |
dc.publisher | Revista De Saude Publica | |
dc.relation | 2016 International Joint Conference On Neural Networks (IJCNN) | |
dc.rights | Acesso restrito | |
dc.subject | Network Segmentation | |
dc.subject | Brain | |
dc.title | A hybrid neural system for the automatic segmentation of the interventricular septum in echocardiographic images | |
dc.type | Trabalho apresentado em evento | |