V-FCNN: Volumetric Fully Convolution Neural Network for Automatic Atrial Segmentation

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7 Citations (Scopus)


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 ( 640×640 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.
Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
EditorsAlistair Young, Kawal Rhode, Maxime Sermesant, Kristin McLeod, Mihaela Pop, Tommaso Mansi, Shuo Li, Jichao Zhao
Number of pages9
Publication statusPublished - 14 Feb 2019

Publication series

NameLecture Notes in Computer Science


  • Anatomy
  • Atria
  • Cardiac imaging
  • Clinical biomarkers
  • Deep learning
  • FCNN
  • Fibrillation
  • MRI scanner
  • Segmentation
  • Shape


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