TransFusion: Efficient Vision Transformer based on 3D transesophageal echocardiography images for the left atrial appendage segmentation

3D transesophageal echocardiography (TEE) is widely used in the preoperative guidance of left atrial appendage closure (LAAC), and is the preferred imaging examination recommended by expert consensus. The precise extraction of the left atrial appendage (LAA) is the essential initial step for establi...

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Bibliographic Details
Published in:	Expert systems with applications Vol. 255; p. 124727
Main Authors:	Wu, Musheng, Zhang, Dan, Hua, Yuejiao, Si, Mateng, Liu, Peng, Wang, Qing
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01-12-2024
Subjects:	3D medical image segmentation 3D transesophageal echocardiography Dual feature fusion Left atrial appendage Transformer 3D transesophageal echocardiography Dual feature fusion Left atrial appendage Transformer 3D medical image segmentation
Online Access:	Get full text
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Summary:	3D transesophageal echocardiography (TEE) is widely used in the preoperative guidance of left atrial appendage closure (LAAC), and is the preferred imaging examination recommended by expert consensus. The precise extraction of the left atrial appendage (LAA) is the essential initial step for establishing an automated predictive procedure of the LAAC. However, due to the inherent limitations of the ultrasound images and the unique morphological features of the LAA, segmenting the LAA in TEE images is a challenging task. In this paper, we propose a novel Transformer-based dual feature fusion network (TransFusion) for LAA segmentation in TEE images. Our TransFusion includes both intra-stage and inter-stage multi-scale feature fusions. Specifically, in each stage of the encoder, we introduce a Multi-Scale Vision Transformer (MSViT) block for extracting and enhancing multi-scale feature representations. Additionally, we use a Feature Fusion module (FFM) to fuse the features across different stages in parallel and alleviate the semantic gap and better recovering the spatial details. Finally, the convolutional neural network (CNN) as the decoder is used to output the results of LAA segmentation. We compare our method with several state-of-the-art methods on our private clinical dataset. Experimental results demonstrate the superior robustness and segmentation performance of our method and its capability to accurately extract the LAA. •A deep learning network for segmenting the left atrial appendage in 3D TEE image.•We extract the multi-scale tokens and calculate them in a cascading manner.•We design a multi-branch parallel fusion method for fusing different stages features.•Our TransFusion achieves best performance in segmenting the left atrial appendage.
ISSN:	0957-4174
DOI:	10.1016/j.eswa.2024.124727