# name
Evaluation with an Independent Dataset of a Deep Learning-based Left Atrium Segmentation Method

# metadata.dc.description[].value
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director/s: Gaspar Delso i Roser Sala. Tutor: Manel Puig

# metadata.dc.description.abstract[].value
Atrial fibrillation (AF) is the most prevalent type of arrhythmia nowadays. Even though it is
associated with significant morbidity and mortality, there is still a substantial lack of basic
understanding of the left atrium (LA) and pulmonary veins (PVs) anatomical structure that curbs
the performance of current clinical treatments for the disease. Thus, segmentation and 3D
reconstruction of the LA and PVs are of crucial importance for the diagnosis and treatment of AF.
In this context, cardiac 3D Late Gadolinium Magnetic Resonance Imaging (LGE-MRI) appear as a
very good tool for cardiac tissue characterization and myocardial fibrosis detection. In fact, these
images have been proofed as reliable predictors of catheter ablation success, which is often the
chosen treatment for AF patients.
Several manual and semi-automatic segmentation tools from LGE-MRI scans are currently in use,
but these are very time-consuming and highly prone to errors, hence the need for an automatic
segmentation approach.
With the rise of deep learning and convolutional neural networks, a number of automatic schemes
are being developed. In this project, we evaluate a model that has been developed at the Hospital
Clínic de Barcelona for obtaining an automatic segmentation of the LA using a deep learning
architecture. Concretely, we tested this model with an independent set of images from another MRI
vendor, and we obtained a set of quantitative and qualitative measures to validate the results.
For the pursuit of our aims, this work begins with the state-of-the-art for LA segmentation of LGEMRI
scans and with a market analysis of the field. We then present our proposed solution together
with the obtained results and the corresponding conclusions.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Nadal Pellisé, Andrea, 2021

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Biomèdica

# metadata.dc.title[].value
Evaluation with an Independent Dataset of a Deep Learning-based Left Atrium Segmentation Method