Deep Learning for Segmentation of the Left Ventricle in Echocardiography

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Ferraz, S (Autor) Outra; Coimbra, M (Autor) FCUP; Pedrosa, J (Autor) Outra

Ata de Conferência Internacional

Título: 2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Páginas: 159-162

IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)

Porto, PORTUGAL, JUN 22-23, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Y-NYA

DOI: 10.1109/enbeng58165.2023.10175322

Abstract (EN): Two-dimensional echocardiography is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Accurate segmentation of the left ventricle in echocardiography is vital for ensuring the accuracy of subsequent diagnosis. Currently, numerous efforts have been made to automatize this task and various public datasets have been released in recent decades to further develop present research. However, medical datasets acquired at different institutions have inherent bias caused by various confounding factors, such as operation policies, machine protocols, treatment preference, etc. As a result, models trained on one dataset, regardless of volume, cannot be confidently utilized for the others. In this study, we investigated model robustness to dataset bias using two publicly available echocardiographic datasets. This work validates the efficacy of a supervised deep learning model for left ventricle segmentation and ejection fraction prediction, outside the dataset on which it was trained. The exposure of this model to unseen, but related samples without additional training maintained a good performance. However, a performance decrease from the original results can be observed, while the impact of quality is also noteworthy with lower quality data leading to decreased performance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4