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PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles

文献类型： 会议论文

第一作者： Manuel Perez-Pelegri

作者： Manuel Perez-Pelegri 1 ; Irene Del-Canto 1 ; Jose V. Monmeneu 2 ; Vicente Bodi 3 ; Maria P. Lopez-Lereu 2 ; David Moratal 1 ; Silvia Ruiz-Espana 1 ;

作者机构： 1.Center for Biomaterials and Tissue Engineering Universitat Politecnica de Valencia

2.Unidad de Imagen Cardiaca ERESA-ASCIRES Grupo Biomedico

3.Department of Medicine Universitat de Valencia

关键词： MRI;U-net;PSP;Segmentation;Deep learning;Left ventricle;Right ventricle;Volume estimation

会议名称： IEEE International Conference on Bioinformatics and Bioengineering

主办单位：

页码： 1048-1053

摘要： Characterization of the heart anatomy and function is mostly done with magnetic resonance image cine series. To achieve a correct characterization, the volume of the right and left ventricle need to be segmented, which is a time-consuming task. We propose a new convolutional neural network architecture that combines U-net with PSP modules (PSPU-net) for the segmentation of left and right ventricle cavities and left ventricle myocardium in the diastolic frame of short-axis cine MRI images and compare its results against a classic 3D U-net architecture. We used a dataset containing 399 cases in total. The results showed higher quality results in both segmentation and final volume estimation for a test set of 99 cases in the case of the PSPU-net, with global dice metrics of 0.910 and median absolute relative errors in volume estimations of 0.026 and 0.039 for the left ventricle cavity and myocardium and 0.051 for the right ventricles cavity.

分类号： Q811.4-53

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