A Hybrid Neural System for the Automatic Segmentation of the Interventricular Septum in Echocardiographic Images

Autor(es)

Baracho, Salety Ferreira ; Leal Pinheiro, Daniel Jose Lins ; de Melo, Vinicius Veloso ; Coelho, Regina Celia ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 7-pg., 2016-01-01.

Resumo

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. (AU)

• Processo FAPESP: 16/04946-4 - Genetic and Evolutionary Computation Conference (GECCO 2016)
• Beneficiário: Vinícius Veloso de Melo
• Modalidade de apoio: Auxílio à Pesquisa - Reunião - Exterior