Mitral Valve Leaflets Segmentation in Echocardiography using Convolutional Neural Networks

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Costa, E (Author) Other; Martins, N (Author) Other; Sultan, MS (Author) Other; Veiga, D (Author) Other; Ferreira, M (Author) Other; Mattos, S (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Proceedings

6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019

22 February 2019 through 23 February 2019

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 16 Citations

Other information

Authenticus ID: P-00Q-K38

DOI: 10.1109/enbeng.2019.8692573

Abstract (EN): Rheumatic heart disease remains a major burden in the developing countries. The World Heart Federation proposed guidelines for the echocardiographic detection of the disease, in which the mitral leaflets' morphology assessment is a key indicator. The drawback is that these guidelines are dependent on the clinician experience. To overcome this limitation, we propose an automatic segmentation of the mitral leaflets using a new method based on convolutional neural network, specifically the UNet architecture. The results indicate a median DICE coefficient of 0.74 in PLAX and 0.79 in A4C for the anterior mitral leaflet segmentation, while median DICE of 0.60 in PLAX and 0.69 A4C are met for the posterior leaflet. A visual evaluation of this segmentation approach by two cardiologists is in line with the numerical results. The false detection due to overestimation and artifacts remains an issue to be addressed in the future.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4