Atrial scar quantification via multi-scale CNN in the graph-cuts framework

Autor: Jennifer Keegan, Guang Yang, Lei Li, David N. Firmin, Lingchao Xu, Tom Wong, Fuping Wu, Raad Mohiaddin, Xiahai Zhuang
Přispěvatelé: British Heart Foundation
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer science
Image quality
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Initialization
Contrast Media
Health Informatics
Dice
LGE MRI
Convolutional neural network
09 Engineering
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
Multi-scale CNN
Cicatrix
0302 clinical medicine
Cut
medicine
Image Processing
Computer-Assisted
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Heart Atria
Graph learning
11 Medical and Health Sciences
Radiological and Ultrasound Technology
medicine.diagnostic_test
business.industry
Magnetic resonance imaging
Pattern recognition
Computer Graphics and Computer-Aided Design
Atrial fibrillation
Magnetic Resonance Imaging
Nuclear Medicine & Medical Imaging
Pulmonary Veins
Left atrium
Scar segmentation
Catheter Ablation
Graph (abstract data type)
Computer Vision and Pattern Recognition
Artificial intelligence
Neural Networks
Computer
business
030217 neurology & neurosurgery
Zdroj: Medical Image Analysis
ISSN: 1361-8423
1361-8415
Popis: Highlights • Propose a fully automatic method for left atrial scar quantification, with promising performance. • Formulate a new framework of scar quantification based on surface projection and graph-cuts framework. • Propose the multi-scale learning CNN, combined with the random shift training strategy, to learn and predict the graph potentials, which significantly improves the performance of the proposed method, and enables the full automation of the framework. • Provide thorough validation and parameter studies for the proposed techniques using fifty-eight clinical images.
Graphical abstract The proposed framework for left atrium (LA) scar quantication and analysis. In this framework, we rst use a well-developed multi-atlas whole heart segmentation (MA-WHS) to obtain an initial segmentation of the LA. Then, we project the LA myocardium to generate a surface mesh, where the quantication is performed. At the same time, both the texture and anatomical features of the LA endocardium can be adequately extracted by employing the multi-scale patch (MSP) strategy. Thus, the features of the nodes in the graph are represented by a set of MSPs. Finally, the labeling of scars is achieved by optimizing a cost function based on the graph-cuts framework, whose potentials for edge weights are explicitly learned and predicted by the proposed multi-scale convolutional neural network (MS-CNN).
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p
Databáze: OpenAIRE
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