Echocardiography Segmentation With Enforced Temporal Consistency
| Autor: | Nathan Painchaud, Nicolas Duchateau, Olivier Bernard, Pierre-Marc Jodoin |
|---|---|
| Přispěvatelé: | Painchaud, Nathan, Département d'informatique [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.) |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] Computer Science - Machine Learning left ventricle Computer Vision and Pattern Recognition (cs.CV) [INFO.INFO-IM] Computer Science [cs]/Medical Imaging Computer Science - Computer Vision and Pattern Recognition Machine Learning (cs.LG) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Image Processing Computer-Assisted FOS: Electrical engineering electronic engineering information engineering myocardium [INFO.INFO-IM]Computer Science [cs]/Medical Imaging Humans variational autoencoder cardiac segmentation Electrical and Electronic Engineering Observer Variation Radiological and Ultrasound Technology ultrasound Image and Video Processing (eess.IV) [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Heart Deep learning [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] Electrical Engineering and Systems Science - Image and Video Processing Computer Science Applications [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] Echocardiography [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Neural Networks Computer CNN Software |
| Zdroj: | IEEE Transactions on Medical Imaging IEEE Transactions on Medical Imaging, 2022, 41 (10), pp.2867-2878. ⟨10.1109/TMI.2022.3173669⟩ |
| ISSN: | 1558-254X 0278-0062 |
| Popis: | Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints. Our method is a post-processing that takes as input segmented echocardiographic sequences produced by any state-of-the-art method and processes it in two steps to (i) identify spatio-temporal inconsistencies according to the overall dynamics of the cardiac sequence and (ii) correct the inconsistencies. The identification and correction of cardiac inconsistencies relies on a constrained autoencoder trained to learn a physiologically interpretable embedding of cardiac shapes, where we can both detect and fix anomalies. We tested our framework on 98 full-cycle sequences from the CAMUS dataset, which are available alongside this paper. Our temporal regularization method not only improves the accuracy of the segmentation across the whole sequences, but also enforces temporal and anatomical consistency. 12 pages, accepted for publication in IEEE TMI |
| Databáze: | OpenAIRE |
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