Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.

EasyChair Preprint no. 772

Abstract

Mitral valve segmentation is a crucial first step to establish a machine learning pipeline that can support practitioners into performing the diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a neural network low-dimension matrix factorization of the echocardiography video. The method is evaluated in a collection of echocardiography video of patients with a variety of mitral valve diseases and exceeds the state-of-the-art method in all the metrics considered.

Keyphrases: Echocardiography, Mitral valve segmentation, Neural network matrix factorization

@Booklet{EasyChair:772,
  author = {Luca Corinzia and Jesse Provost and Alessandro Candreva and Maurizio Tamarasso and Francesco Maisano and Joachim M. Buhmann},
  title = {Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.},
  howpublished = {EasyChair Preprint no. 772},
  doi = {10.29007/6kbt},
  year = {EasyChair, 2019}}