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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

26.02.2020 | Original Article

Segmentation and visualization of left atrium through a unified deep learning framework

verfasst von: Xiuquan Du, Susu Yin, Renjun Tang, Yueguo Liu, Yuhui Song, Yanping Zhang, Heng Liu, Shuo Li

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 4/2020

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Abstract

Purpose

Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause redundant information to be passed to affect segmentation performance. Moreover, they did not further consider atrial visualization after segmentation, which leads to a lack of understanding of the essential atrial anatomy.

Methods

We propose a novel unified deep learning framework for left atrium segmentation and visualization simultaneously. At first, a novel dual-path module is used to enhance the expressiveness of cardiac image representation. Then a multi-scale context-aware module is designed to effectively handle complex appearance and shape variations of the left atrium and associated pulmonary veins. The generated multi-scale features are feed to gated bidirectional message passing module to remove irrelevant information and extract discriminative features. Finally, the features after message passing are efficiently combined via a deep supervision mechanism to produce the final segmentation result and reconstruct 3D volumes.

Results

Our approach primarily against the 2018 left atrium segmentation challenge dataset, which consists of 100 3D gadolinium-enhanced magnetic resonance images. Our method achieves an average dice of 0.936 in segmenting the left atrium via fivefold cross-validation, which outperforms state-of-the-art methods.

Conclusions

The performance demonstrates the effectiveness and advantages of our network for the left atrium segmentation and visualization. Therefore, our proposed network could potentially improve the clinical diagnosis and treatment of atrial fibrillation.

Vorheriger Artikel Recursive multiresolution convolutional neural networks for 3D aortic valve annulus planimetry

Nächster Artikel An integrated multi-objective whale optimized support vector machine and local texture feature model for severity prediction in subjects with cardiovascular disorder

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Titel: Segmentation and visualization of left atrium through a unified deep learning framework
verfasst von: Xiuquan Du
Susu Yin
Renjun Tang
Yueguo Liu
Yuhui Song
Yanping Zhang
Heng Liu
Shuo Li
Publikationsdatum: 26.02.2020
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 4/2020
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-020-02128-9

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Abstract

Purpose

Methods

Results

Conclusions

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