HALSR-Net:利用混合注意和潜在空间重建改进左心室MRI的CNN分割

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Mohamed Fakhfakh , Laurent Sarry , Patrick Clarysse
{"title":"HALSR-Net:利用混合注意和潜在空间重建改进左心室MRI的CNN分割","authors":"Mohamed Fakhfakh ,&nbsp;Laurent Sarry ,&nbsp;Patrick Clarysse","doi":"10.1016/j.compmedimag.2025.102546","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate cardiac MRI segmentation is vital for detailed cardiac analysis, yet the manual process is labor-intensive and prone to variability. Despite advancements in MRI technology, there remains a significant need for automated methods that can reliably and efficiently segment cardiac structures. This paper introduces HALSR-Net, a novel multi-level segmentation architecture designed to improve the accuracy and reproducibility of cardiac segmentation from Cine-MRI acquisitions, focusing on the left ventricle (LV). The methodology consists of two main phases: first, the extraction of the region of interest (ROI) using a regression model that accurately predicts the location of a bounding box around the LV; second, the semantic segmentation step based on HALSR-Net architecture. This architecture incorporates a Hybrid Attention Pooling Module (HAPM) that merges attention and pooling mechanisms to enhance feature extraction and capture contextual information. Additionally, a reconstruction module leverages latent space features to further improve segmentation accuracy. Experiments conducted on an in-house clinical dataset and two public datasets (ACDC and LVQuan19) demonstrate that HALSR-Net outperforms state-of-the-art architectures, achieving up to 98% accuracy and F1-score for the segmentation of the LV cavity and myocardium. The proposed approach effectively addresses the limitations of existing methods, offering a more accurate and robust solution for cardiac MRI segmentation, thereby likely to improve cardiac function analysis and patient care.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"123 ","pages":"Article 102546"},"PeriodicalIF":5.4000,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HALSR-Net: Improving CNN Segmentation of Cardiac Left Ventricle MRI with Hybrid Attention and Latent Space Reconstruction\",\"authors\":\"Mohamed Fakhfakh ,&nbsp;Laurent Sarry ,&nbsp;Patrick Clarysse\",\"doi\":\"10.1016/j.compmedimag.2025.102546\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate cardiac MRI segmentation is vital for detailed cardiac analysis, yet the manual process is labor-intensive and prone to variability. Despite advancements in MRI technology, there remains a significant need for automated methods that can reliably and efficiently segment cardiac structures. This paper introduces HALSR-Net, a novel multi-level segmentation architecture designed to improve the accuracy and reproducibility of cardiac segmentation from Cine-MRI acquisitions, focusing on the left ventricle (LV). The methodology consists of two main phases: first, the extraction of the region of interest (ROI) using a regression model that accurately predicts the location of a bounding box around the LV; second, the semantic segmentation step based on HALSR-Net architecture. This architecture incorporates a Hybrid Attention Pooling Module (HAPM) that merges attention and pooling mechanisms to enhance feature extraction and capture contextual information. Additionally, a reconstruction module leverages latent space features to further improve segmentation accuracy. Experiments conducted on an in-house clinical dataset and two public datasets (ACDC and LVQuan19) demonstrate that HALSR-Net outperforms state-of-the-art architectures, achieving up to 98% accuracy and F1-score for the segmentation of the LV cavity and myocardium. The proposed approach effectively addresses the limitations of existing methods, offering a more accurate and robust solution for cardiac MRI segmentation, thereby likely to improve cardiac function analysis and patient care.</div></div>\",\"PeriodicalId\":50631,\"journal\":{\"name\":\"Computerized Medical Imaging and Graphics\",\"volume\":\"123 \",\"pages\":\"Article 102546\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computerized Medical Imaging and Graphics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0895611125000552\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895611125000552","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

准确的心脏MRI分割对于详细的心脏分析至关重要,但手工过程是劳动密集型的,容易发生变化。尽管MRI技术取得了进步,但仍然需要能够可靠有效地分割心脏结构的自动化方法。本文介绍了HALSR-Net,一种新的多级分割架构,旨在提高从Cine-MRI采集的心脏分割的准确性和可重复性,重点是左心室(LV)。该方法包括两个主要阶段:首先,使用回归模型提取感兴趣区域(ROI),该模型准确预测LV周围边界框的位置;第二步,基于HALSR-Net架构的语义分割步骤。该架构结合了一个混合注意力池模块(HAPM),该模块将注意力和注意力池机制合并在一起,以增强特征提取和捕获上下文信息。此外,重构模块利用潜在空间特征进一步提高分割精度。在内部临床数据集和两个公共数据集(ACDC和LVQuan19)上进行的实验表明,HALSR-Net优于最先进的架构,在LV腔和心肌分割方面达到98%的准确率和f1分。该方法有效地解决了现有方法的局限性,为心脏MRI分割提供了更准确、更稳健的解决方案,从而有可能改善心功能分析和患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HALSR-Net: Improving CNN Segmentation of Cardiac Left Ventricle MRI with Hybrid Attention and Latent Space Reconstruction
Accurate cardiac MRI segmentation is vital for detailed cardiac analysis, yet the manual process is labor-intensive and prone to variability. Despite advancements in MRI technology, there remains a significant need for automated methods that can reliably and efficiently segment cardiac structures. This paper introduces HALSR-Net, a novel multi-level segmentation architecture designed to improve the accuracy and reproducibility of cardiac segmentation from Cine-MRI acquisitions, focusing on the left ventricle (LV). The methodology consists of two main phases: first, the extraction of the region of interest (ROI) using a regression model that accurately predicts the location of a bounding box around the LV; second, the semantic segmentation step based on HALSR-Net architecture. This architecture incorporates a Hybrid Attention Pooling Module (HAPM) that merges attention and pooling mechanisms to enhance feature extraction and capture contextual information. Additionally, a reconstruction module leverages latent space features to further improve segmentation accuracy. Experiments conducted on an in-house clinical dataset and two public datasets (ACDC and LVQuan19) demonstrate that HALSR-Net outperforms state-of-the-art architectures, achieving up to 98% accuracy and F1-score for the segmentation of the LV cavity and myocardium. The proposed approach effectively addresses the limitations of existing methods, offering a more accurate and robust solution for cardiac MRI segmentation, thereby likely to improve cardiac function analysis and patient care.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信