CT图像特征点的智能提取改进凸轮型股髋臼撞击评估。

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Sareh Tayyebinezhad, Mansoor Fatehi, Hossein Arabalibeik, Hossein Ghadiri
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引用次数: 0

摘要

目的:具有凸轮型形态的股髋臼撞击(FAI)是一种常见的髋关节疾病,可导致腹股沟疼痛并最终导致骨关节炎。术前评估基于x线或计算机断层扫描(CT)获得的参数,即α角(AA)和股骨头头颈偏移(FHNO)。我们的研究目标是开发一种计算机辅助检测(CAD)系统,从CT扫描中自动选择髋关节区域并测量诊断参数,以克服主观选择CT图像切片获取参数的繁琐和耗时的局限性。材料与方法:回顾性收集2018 - 2022年两家医院普通腹部骨盆CT检查病例271例,每所医院均配备不同的CT扫描仪。首先,设计卷积神经网络(CNN),从腹部骨盆CT扫描图像序列中选择髋关节区域切片;该CNN使用80个CT扫描进行训练,分别分为50%,20%和30%的训练组,验证组和测试组。其次,选择最合适的穿过股骨头颈复合体的斜片,利用图像处理算法计算AA和FHNO标志;人工选择/测量每个髋部的最佳斜片作为基础真值及其相关参数。结果:基于CNN的CT髋部区域选择准确率为99.34%。AA和FHNO人工与自动参数测量的Pearson相关系数分别为0.964和0.856。结论:这项研究的结果为未来开发用于筛查CT扫描的CAD软件应用程序提供了希望,该软件可以帮助医生评估FAI。股髋臼撞击是一种常见的未确诊的髋关节疾病,需要耗时的基于图像的测量。人工智能能提高放射学评估的效率和一致性吗?使用混合人工智能方法的自动切片选择和地标检测提高了测量效率和准确性,通过Bland-Altman分析证实了最小的偏差。基于人工智能的方法可以在常规CT图像中更快,更一致地评估凸轮型股髋臼撞击,支持早期识别并减少对临床工作流程中操作员经验的依赖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent extraction of CT image landmarks for improving cam-type femoroacetabular impingement assessment.

Objectives: Femoroacetabular impingement (FAI) with cam-type morphology is a common hip disorder that can result in groin pain and eventually osteoarthritis. The pre-operative assessment is based on parameters obtained from x-ray or computed tomography (CT) scans, namely alpha angle (AA) and femoral head-neck offset (FHNO). The goal of our study was to develop a computer-aided detection (CAD) system to automatically select the hip region and measure diagnostic parameters from CT scans to overcome the limitations of the tedious and time-consuming process of subjectively selecting CT image slices to obtain parameters.

Materials and methods: 271 cases of ordinary abdominopelvic CT examination were collected retrospectively from two hospitals between 2018 and 2022, each equipped with a distinct CT scanner. First, a convolution neural network (CNN) was designed to select hip region slices among abdominopelvic CT scan image series. This CNN was trained using 80 CT scans divided into 50%, 20%, and 30% for training, validation and testing groups, respectively. Second, the most appropriate oblique slice passing through the femoral head-neck complex was selected, and AA and FHNO landmarks were calculated using image-processing algorithms. The best oblique slices were selected/measured manually for each hip as ground truth and its related parameters.

Results: CT hip-region selection using CNN yielded 99.34% accuracy. Pearson correlation coefficient between manual and automatic parameters measurement were 0.964 and 0.856 for AA and FHNO, respectively.

Conclusion: The results of this study are promising for future development of a CAD software application for screening CT scans that may aid physicians to assess FAI.

Key points: Question Femoroacetabular impingement is a common, underdiagnosed hip disorder requiring time-consuming image-based measurements. Can AI improve the efficiency and consistency of its radiologic assessment? Findings Automated slice selection and landmark detection using a hybrid AI method improved measurement efficiency and accuracy, with minimal bias confirmed through Bland-Altman analysis. Clinical relevance An AI-based method enables faster, more consistent evaluation of cam-type femoroacetabular impingement in routine CT images, supporting earlier identification and reducing dependency on operator experience in clinical workflows.

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来源期刊
European Radiology
European Radiology 医学-核医学
CiteScore
11.60
自引率
8.50%
发文量
874
审稿时长
2-4 weeks
期刊介绍: European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.
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