Echocardiographic mitral valve segmentation model

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chunxia Liu , Shanshan Dong , Feng Xiong , Luqing Wang , Bolun Li , Hongjun Wang
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引用次数: 0

Abstract

Segmentation of mitral valve is not only important for clinical diagnosis, but also has far-reaching impact on prevention and prognosis of the disease by experts and doctors. In this paper, the multi-channel cross fusion transformer based U-Net network model (MCCT-UNet) is proposed according to the classical U-Net architecture. First, the jump connection part of MCCT-UNet is designed by using a multi-channel cross-fusion based attention mechanism module (MCCT) instead of the original jump connection, and this module fuses the feature maps from different scales in different stages of the encoder. Second, the optimization of the feature fusion method is proposed in the decoding stage by designing the cross-compression excitation sub-module (C-SENet) to replace the simple feature splicing, and the C-SENet is used to bridge the inconsistency of the semantic hierarchy by effectively combining the deeper information in the encoding stage with the shallower information. This two modules can establish a close connection between the encoder and decoder by exploring multi-scale global contextual information to solve the semantic divide problem, thus it significantly improves the segmentation performance of the network. The experimental results show that the improvement is effective, and the MCCT-UNet model outperforms the other 9 network models. Specifically, the MCCT-UNet achieved a Dice coefficient of 0.8734, an IoU of 0.7854, and an accuracy of 0.9977, demonstrating significant improvements over the compared models.
超声心动图二尖瓣分割模型
二尖瓣的分割不仅对临床诊断有重要意义,而且对专家和医生预防和预后疾病也有深远影响。本文根据经典的 U-Net 架构,提出了基于多通道交叉融合变压器的 U-Net 网络模型(MCCT-UNet)。首先,MCCT-UNet 的跳转连接部分采用基于多通道交叉融合的注意力机制模块(MCCT)代替原有的跳转连接,该模块在编码器的不同阶段融合不同尺度的特征图。其次,在解码阶段提出了对特征融合方法的优化,设计了交叉压缩激发子模块(C-SENet)来替代简单的特征拼接,通过 C-SENet 将编码阶段的深层信息与浅层信息有效结合,弥合语义层次的不一致性。这两个模块通过探索多尺度的全局上下文信息,在编码器和解码器之间建立了紧密的联系,从而解决了语义鸿沟问题,显著提高了网络的分割性能。实验结果表明,改进效果显著,MCCT-UNet 模型优于其他 9 个网络模型。具体来说,MCCT-UNet 的骰子系数达到了 0.8734,IoU 达到了 0.7854,准确率达到了 0.9977,与其他模型相比有了显著的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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