dc.description.abstract |
Atrial fibrillation (AF), characterised by rapid and irregular electrical activity in the
upper chambers of the heart, is the most common sustained heart rhythm disturbance. AF
is a leading cause of hospitalisation worldwide and a significant risk factor for heart failure
and stroke. AF ablation procedures aim to isolate or disrupt the electrical activity sustaining
AF in order to restore the normal rhythm of the heart. However, these ablation treatments
remain suboptimal with limited success rates, particularly with long-term follow-up. This
outcome is primarily due to a lack of basic understanding of the underlying atrial structure,
particularly features of atrial geometry and fibrosis, the hallmark of structural remodelling,
that directly sustain AF. Additionally, there is an absence of effective tools to create virtual
patient-specific heart models and to provide accurate locations for more targeted ablation.
Late gadolinium contrast-enhancing agents are used in 30% of all MRI (LGE-MRI) scans
worldwide to improve the visibility of structures often associated with disease, such as
fibrosis (scar tissue). Recent clinical studies using LGE-MRI suggest that atrial scar tissue is
an important contributor to the structural substrate for AF, even in patients without cardiac
comorbidities. The series of multi-centre, prospective, observational cohort studies from
the University of Utah have demonstrated that non-invasive evaluation of left atrial (LA)
fibrosis using LGE-MRI is independently associated with ablation outcomes in patients
with AF. More importantly, they have developed an LGE-MRI guided patient selection
approach using fibrosis extent and distribution assessment for patient stratification to either
targeted AF ablation or medical management.
In current medical practice, atrial segmentation from medical images for clinical diagnosis
and treatment is performed by manual tracing of the atrial structures from LGE-MRIs.
This process is often described as time-consuming, labour-intensive and error-prone, lacking
reproducibility and scalability. However, direct automatic segmentation and analysis of
the atrial structures from LGE-MRIs are challenging due to the complex atrial geometry,
attenuated contrast and low spatial resolution of the in vivo LGE-MRIs, limiting the accuracy
of traditional segmentation algorithms.
In this thesis, we proposed and extensively tested a novel deep learning approach using
a two-stage convolutional neural network architecture for direct automatic segmentation
of the LA and fibrosis. First, we proposed an in-depth investigation of the conventional
methods, along with state-of-the-art deep learning approaches to perform LA segmentation.
This analysis extensively investigated the essential aspects of LA segmentation using deep
learning, the various existing approaches and their respective shortcomings. Based on the
results and insights of our investigations, we successfully developed and optimised a robust
deep learning model to perform automatic LA segmentation using the largest LGE-MRIs
dataset currently available (from the University of Utah). Then, we assessed the efficacy of
our deep learning approach by testing it on a differential clinical dataset, translating from
research development to clinical application. For this purpose, we recruited 11 patients
with AF fromWaikato Hospital scheduled for AF ablation to undergo LGE-MRI. Finally,
we conducted a detailed analysis of four different established methods for automatic scar
tissue segmentation from LGE-MRIs, and compared the results obtained with our deep
learning results when applied for LA fibrosis segmentation.
This study demonstrates a promising framework to provide a 3D virtual heart including
the characterisation of the key structural remodelling features implicated in AF directly
from clinical LGE-MRIs. Our investigations successfully lead to the development of an
efficient and robust deep learning approach for automatic LA segmentation and provide
critical insights regarding the evaluation of fibrosis extent in the LA wall. This study also
presents a sound basis for further investigations into new research questions regarding
LA and fibrosis segmentation using a deep learning model. The conclusions from our
computational framework may serve as an immediate foundation for future work on
AF patient stratification, potentially patient-tailored diagnosis, ablation treatment and
prognosis, to treat the most common cardiac arrhythmia seen in clinics more effectively. |
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