A hybrid neural system for the automatic segmentation of the interventricular septum in echocardiographic images

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2016
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Valença, Janaina Bussola Montrezor [UNIFESP]
Ferraz, Karoline Pereira [UNIFESP]
Alencar, Maria do Carmo Baracho de [UNIFESP]
Souza, Felipe Granado [UNIFESP]
Lopes, Lucy Vitale
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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.
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2016 International Joint Conference On Neural Networks (IJCNN). New york, p. 5072-5078, 2016.