A computer vision pipeline for fully automated echocardiogram interpretation

Lane, Elisabeth (2022) A computer vision pipeline for fully automated echocardiogram interpretation. Doctoral thesis, University of West London.

[thumbnail of Elisabeth Lane - Final PhD Thesis (Dec 22).pdf]
Elisabeth Lane - Final PhD Thesis (Dec 22).pdf - Accepted Version

Download (31MB) | Preview


Cardiovascular disease is the leading cause of global mortality and continues to place a significant burden, in economic and resource terms, upon health services. A 2-dimensional transthoracic echocardiogram captures high spatial and temporal images and videos of the heart and is the modality of choice for the rapid assessment of heart function and structure due to it’s non-invasive nature and lack of ionising radiation.
The challenging process of analysing echocardiographic images is currently manually performed by trained experts, though this process is vulnerable to intra- and inter-observer variability and is highly time-consuming. Additionally, echocardiographic images suffer from varying degrees of noise and vary drastically in terms of image quality.
Exponential advancements in the fields of artificial intelligence, deep learning and computer vision have enabled the rapid development of automated systems capable of high-precision tasks, often out-performing human experts. This thesis aims to investigate the applicability of applying deep learning methods to automate key processes in the modern echocardiographic laboratory. Namely, view classification, quality assessment, cardiac phase detection, segmentation of the left ventricle and keypoint detection on tissue Doppler imaging strips.
State-of-the-art deep learning architectures were applied to each task, and evaluated against ground-truth annotations provided by trained experts. The datasets used throughout each Chapter are diverse and, in some cases, have been made public for the benefit of the research community. To encourage transparency and openness, all code and model weights have been published.
Should automated deep learning systems, both online (in terms of providing real-time feedback) and offline (behind the scenes), become integrated within clinical practice, there is great potential for improved accuracy and efficiency, thus improving patient outcomes. Furthermore, health services could save valuable resources such as time and money.

Item Type: Thesis (Doctoral)
Identifier: 10.36828/szxf7275
Subjects: Computing > Intelligent systems
Depositing User: Jisc Router
Date Deposited: 28 Feb 2023 11:16
Last Modified: 28 Feb 2023 17:00
URI: https://repository.uwl.ac.uk/id/eprint/9819


Downloads per month over past year

Actions (login required)

View Item View Item