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Reunião Anual Conjunta dos Grupos de Estudo de Cirurgia Cardíaca, Doenças Valvulares e Ecocardiografia da SPC
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Automatic Multi-View Pose Estimation in Focused Cardiac Ultrasound
Session:
Posters (Sessão 2 - Écran 7) - Ecocardiografia
Speaker:
João Freitas
Congress:
CPC 2023
Topic:
B. Imaging
Theme:
03. Imaging
Subtheme:
03.1 Echocardiography
Session Type:
Pósters Electrónicos
FP Number:
---
Authors:
João Freitas; João Gomes-Fonseca; Cátia Oliveira; Vítor Hugo-Pereira; Jorge Correia-Pinto; Jaime C. Fonseca; Sandro Queirós
Abstract
<p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><strong>Introduction:</strong> Focused cardiac ultrasound (FoCUS) is an unvaluable tool at the bedside in the assessment of patients with acute/critical conditions. However, compared to a conventional echocardiography exam, FoCUS differs in the equipment used (inferior quality), in the examination scope (limited set of views) and in the operators (usually less experienced), which make FoCUS a primarily qualitative (bidimensional) exam.</span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><strong>Objective:</strong> To develop an algorithm that automatically estimates the spatial relationship between five standard FoCUS cardiac views. The relative pose between views would allow to represent all in the same three-dimensional coordinate system, thus mitigating the major barrier towards the application of 3D quantitative cardiac image analysis methods to FoCUS.</span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><strong>Methods:</strong> An automatic pipeline for the generation of realistically looking (synthetic) FoCUS datasets, with both image and pose data, was developed using an ultrasound simulator and an image-to-image translation method. Leveraging of the created dataset, a novel framework for pose estimation contemplating three stages was implemented. In the first stage, a convolutional neural network based on an encoder-decoder architecture is proposed to regress line-based heatmaps representing the most likely areas of intersection between input images. In the second stage, the lines that best fit the regressed heatmaps are extracted through a multi-resolution grid search algorithm. In the final stage, the previously identified lines are used to create a system of non-linear equations, whose solution traduces the relative 3D pose between all input views.</span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><strong>Results and Conclusion:</strong> Overall, the developed heatmap regression method proved to be feasible and accurate, outperforming the implemented baselines in all evaluation metrics. Similarly, the 3D view positioning method showed its feasibility for pose estimation. Altogether, the results for the developed framework are promising, suggesting its usefulness in a future clinical context.</span></span></p>
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