Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography.

IF 4.1 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Maike Theis, Laura Garajová, Babak Salam, Sebastian Nowak, Wolfgang Block, Ulrike I Attenberger, Daniel Kütting, Julian A Luetkens, Alois M Sprinkart
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引用次数: 0

Abstract

Objectives: Recently, epicardial adipose tissue (EAT) assessed by CT was identified as an independent mortality predictor in patients with various cardiac diseases. Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT.

Methods: Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included. Slice selection at aortic valve (AV)-level and EAT segmentation were performed manually as ground truth. For slice extraction, two approaches were compared: A regression model with a 2D convolutional neural network (CNN) and a 3D CNN utilizing reinforcement learning (RL). Performance evaluation was based on mean absolute z-deviation to the manually selected AV-level (Δz). For tissue segmentation, a 2D U-Net was trained on single-slice images at AV-level and compared to the open-source body and organ analysis (BOA) framework using Dice score. Superior methods were selected for end-to-end evaluation, where mean absolute difference (MAD) of EAT area and tissue density were compared. 95% confidence intervals (CI) were assessed for all metrics.

Results: Slice extraction using RL was slightly more precise (Δz: RL 1.8 mm (95% CI: [1.6, 2.0]), 2D CNN 2.0 mm (95% CI: [1.8, 2.3])). For EAT segmentation at AV-level, the 2D U-Net outperformed BOA significantly (Dice score: 2D U-Net 91.3% (95% CI: [90.7, 91.8]), BOA 85.6% (95% CI: [84.7, 86.5])). The end-to-end evaluation revealed high agreement between automatic and manual measurements of EAT (MAD area: 1.1 cm2 (95% CI: [1.0, 1.3]), MAD density: 2.2 Hounsfield units (95% CI: [2.0, 2.5])).

Conclusions: We propose a method for robust automatic EAT assessment in spiral CT scans enabling opportunistic evaluation in clinical routine.

Critical relevance statement: Since inflammatory changes in epicardial adipose tissue (EAT) are associated with an increased risk of cardiac diseases, automated evaluation can serve as a basis for developing automated cardiac risk assessment tools, which are essential for efficient, large-scale assessment in opportunistic settings.

Key points: Deep learning methods for automatic assessment of epicardial adipose tissue (EAT) have great potential. A 2-step approach with slice extraction and tissue segmentation enables robust automated evaluation of EAT. End-to-end automation enables large-scale research on the value of EAT for outcome analysis.

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来源期刊
Insights into Imaging
Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
7.30
自引率
4.30%
发文量
182
审稿时长
13 weeks
期刊介绍: Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.
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