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Erschienen in:
2019 | OriginalPaper | Buchkapitel
V-FCNN: Volumetric Fully Convolution Neural Network for Automatic Atrial Segmentation
verfasst von : Nicoló Savioli, Giovanni Montana, Pablo Lamata
Erschienen in: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
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Abstract
Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable for the definition of clinical biomarkers. There is thus a need for its fully automatic segmentation from clinical images. This work presents an architecture based on 3D-convolution kernels, a Volumetric Fully Convolution Neural Network (V-FCNN), able to segment the entire atrial anatomy in a one-shot from high-resolution images (\(640\times 640\) pixels). A loss function based on the mixture of both Mean Square Error (MSE) and Dice Loss (DL) is used, in an attempt to combine the ability to capture the bulk shape as well as the reduction of local errors caused by over-segmentation.
Results demonstrate a good performance in the middle region of the atria along with the challenges impact of capturing the pulmonary veins variability or valve plane identification that separates the atria to the ventricle. Despite the need to reduce the original image resolution to fit into Graphics Processing Unit (GPU) hardware constraints, \(92.5\%\) and \(85.1\%\) were obtained respectively in the 2D and 3D Dice metric in 54 test patients (4752 atria test slices in total), making the V-FCNN a reasonable model to be used in clinical practice.