利用全卷积网络从磁共振成像中自动分割左心室,研究乳腺癌患者的 CTRCD。

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Medical Imaging Pub Date : 2024-03-01 Epub Date: 2024-03-19 DOI:10.1117/1.JMI.11.2.024003
Julia Kar, Michael V Cohen, Samuel A McQuiston, Teja Poorsala, Christopher M Malozzi
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引用次数: 0

摘要

目的:本研究的目的是开发一种全卷积网络(FCN)工具,用于在刺激回波核磁共振成像的位移编码中自动分割左心室心肌。分割结果用于对易受癌症治疗相关心功能障碍(CTRCD)影响的乳腺癌患者进行左心室腔室定量和应变分析。方法:定制设计了以 ResNet-101 为骨干的 DeepLabV3+ FCN,在 45 个女性乳腺癌数据集(23 个训练集、11 个验证集和 11 个测试集)上进行腔室量化。测量了左心室结构参数和左心室射血分数(LVEF),并采用径向点插值法估算了心肌应变。心肌分类验证是针对基于量化的地面实况,计算准确度、Dice评分、平均垂直距离(APD)、豪斯多夫距离等。此外,还通过等效性测试和 Cronbach's alpha(C-α)类内相关系数对 FCN 和供应商的心腔量化及心肌应变计算工具进行了验证。结果:与地面实况相比,验证集的心肌分类结果为:Dice=0.89,APD=2.4 mm,准确率=97%;测试集的心肌分类结果为:Dice=0.90,APD=2.5 mm,准确率=97%。FCN和供应商工具之间等效性检验的置信区间(CI)和两个单侧t检验结果为:LVEF(58±5%对57±6%)的置信区间(CI)=-1.36%至2.42%,p值<0.001;纵向应变(-15±2%对-15±3%)的置信区间(CI)=-0.71%至0.63%,p值<0.001。结论验证结果与基于供应商工具的参数估计结果相当,这表明采用我们提出的 FCN 方法可实现准确的左心室腔量化,然后进行应变分析,以进行 CTRCD 调查。
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Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients.

Purpose: The goal of this study was to develop a fully convolutional network (FCN) tool to automatedly segment the left-ventricular (LV) myocardium in displacement encoding with stimulated echoes MRI. The segmentation results are used for LV chamber quantification and strain analyses in breast cancer patients susceptible to cancer therapy-related cardiac dysfunction (CTRCD). Approach: A DeepLabV3+ FCN with a ResNet-101 backbone was custom-designed to conduct chamber quantification on 45 female breast cancer datasets (23 training, 11 validation, and 11 test sets). LV structural parameters and LV ejection fraction (LVEF) were measured, and myocardial strains estimated with the radial point interpolation method. Myocardial classification validation was against quantization-based ground-truth with computations of accuracy, Dice score, average perpendicular distance (APD), Hausdorff-distance, and others. Additional validations were conducted with equivalence tests and Cronbach's alpha (C-α) intraclass correlation coefficients between the FCN and a vendor tool on chamber quantification and myocardial strain computations. Results: Myocardial classification results against ground-truth were Dice=0.89, APD=2.4  mm, and accuracy=97% for the validation set and Dice=0.90, APD=2.5  mm, and accuracy=97% for the test set. The confidence intervals (CI) and two one-sided t-test results of equivalence tests between the FCN and vendor-tool were CI=-1.36% to 2.42%, p-value < 0.001 for LVEF (58±5% versus 57±6%), and CI=-0.71% to 0.63%, p-value < 0.001 for longitudinal strain (-15±2% versus -15±3%). Conclusions: The validation results were found equivalent to the vendor tool-based parameter estimates, which show that accurate LV chamber quantification followed by strain analysis for CTRCD investigation can be achieved with our proposed FCN methodology.

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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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