基于深度学习和统计形状建模的前交叉韧带重建中髁间切迹体积评估方法

IF 1.6 4区 医学 Q3 ORTHOPEDICS
Knee Pub Date : 2025-02-28 DOI:10.1016/j.knee.2025.02.009
Anna Ghidotti , Daniele Regazzoni , Miri Weiss Cohen , Caterina Rizzi , Vincenzo Condello
{"title":"基于深度学习和统计形状建模的前交叉韧带重建中髁间切迹体积评估方法","authors":"Anna Ghidotti ,&nbsp;Daniele Regazzoni ,&nbsp;Miri Weiss Cohen ,&nbsp;Caterina Rizzi ,&nbsp;Vincenzo Condello","doi":"10.1016/j.knee.2025.02.009","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Anterior cruciate ligament (ACL) reconstruction is a widely performed procedure for ACL injury, but there are several factors which may lead to re-rupture or clinical failure. An intercondylar notch (or fossa) that is narrower may increase the likelihood of injury. Traditional two-dimensional assessments are limited, and three-dimensional (3D) volume analysis may offer more detailed insights. This study employs deep learning and statistical shape modeling (SSM) to enhance 3D modeling of the intercondylar notch, aiming to gain a deeper understanding of this complex 3D anatomical region.</div></div><div><h3>Methods</h3><div>A methodology was developed to generate accurate 3D models of the intercondylar fossa within seconds. The variability of the intercondylar notch in ACL-injured samples was analyzed using SSM techniques, focusing on its principal components. Additionally, gender differences in notch volume were examined using <em>t</em>-tests.</div></div><div><h3>Results</h3><div>The best deep learning method for automatic segmentation of the notch was SegResNet, which achieved a Dice similarity coefficient of over 0.88 and a Hausdorff distance of 0.73 mm. The small volume-related relative error (0.06) illustrates the goodness of the result. Three principal components accounted for 72.59% of the variation, including notch volume, shape, width, and height. Females had statistically significant smaller notch compared with males with ACL injury (<em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>By examining notch volume and its variability in ACL-injured patients, it is possible to understand the complex anatomy of the intercondylar notch and tailor ACL reconstructions accordingly.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"54 ","pages":"Pages 71-80"},"PeriodicalIF":1.6000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep learning and statistical shape modeling-based method for assessing intercondylar notch volume in anterior cruciate ligament reconstruction\",\"authors\":\"Anna Ghidotti ,&nbsp;Daniele Regazzoni ,&nbsp;Miri Weiss Cohen ,&nbsp;Caterina Rizzi ,&nbsp;Vincenzo Condello\",\"doi\":\"10.1016/j.knee.2025.02.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Anterior cruciate ligament (ACL) reconstruction is a widely performed procedure for ACL injury, but there are several factors which may lead to re-rupture or clinical failure. An intercondylar notch (or fossa) that is narrower may increase the likelihood of injury. Traditional two-dimensional assessments are limited, and three-dimensional (3D) volume analysis may offer more detailed insights. This study employs deep learning and statistical shape modeling (SSM) to enhance 3D modeling of the intercondylar notch, aiming to gain a deeper understanding of this complex 3D anatomical region.</div></div><div><h3>Methods</h3><div>A methodology was developed to generate accurate 3D models of the intercondylar fossa within seconds. The variability of the intercondylar notch in ACL-injured samples was analyzed using SSM techniques, focusing on its principal components. Additionally, gender differences in notch volume were examined using <em>t</em>-tests.</div></div><div><h3>Results</h3><div>The best deep learning method for automatic segmentation of the notch was SegResNet, which achieved a Dice similarity coefficient of over 0.88 and a Hausdorff distance of 0.73 mm. The small volume-related relative error (0.06) illustrates the goodness of the result. Three principal components accounted for 72.59% of the variation, including notch volume, shape, width, and height. Females had statistically significant smaller notch compared with males with ACL injury (<em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>By examining notch volume and its variability in ACL-injured patients, it is possible to understand the complex anatomy of the intercondylar notch and tailor ACL reconstructions accordingly.</div></div>\",\"PeriodicalId\":56110,\"journal\":{\"name\":\"Knee\",\"volume\":\"54 \",\"pages\":\"Pages 71-80\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2025-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knee\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0968016025000225\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knee","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968016025000225","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0

摘要

前交叉韧带(ACL)重建是一种广泛应用于前交叉韧带损伤的手术,但有几个因素可能导致再次断裂或临床失败。较窄的髁间切迹(或窝)可能增加受伤的可能性。传统的二维评估是有限的,而三维(3D)体积分析可能提供更详细的见解。本研究采用深度学习和统计形状建模(SSM)来增强髁间切迹的三维建模,旨在更深入地了解这一复杂的三维解剖区域。方法建立了一种在数秒内生成准确的髁间窝三维模型的方法。使用SSM技术分析acl损伤样本髁间切迹的变异性,重点分析其主要成分。此外,缺口体积的性别差异采用t检验。结果SegResNet是自动分割缺口的最佳深度学习方法,其Dice相似系数大于0.88,Hausdorff距离为0.73 mm。与体积相关的相对误差很小(0.06)说明了结果的良好性。缺口体积、形状、宽度和高度3个主成分占变异的72.59%。女性前交叉韧带损伤切口较男性小,差异有统计学意义(P <;0.001)。结论通过检查ACL损伤患者的切迹体积及其变异性,可以了解髁间切迹的复杂解剖结构,从而有针对性地进行ACL重建。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep learning and statistical shape modeling-based method for assessing intercondylar notch volume in anterior cruciate ligament reconstruction

Background

Anterior cruciate ligament (ACL) reconstruction is a widely performed procedure for ACL injury, but there are several factors which may lead to re-rupture or clinical failure. An intercondylar notch (or fossa) that is narrower may increase the likelihood of injury. Traditional two-dimensional assessments are limited, and three-dimensional (3D) volume analysis may offer more detailed insights. This study employs deep learning and statistical shape modeling (SSM) to enhance 3D modeling of the intercondylar notch, aiming to gain a deeper understanding of this complex 3D anatomical region.

Methods

A methodology was developed to generate accurate 3D models of the intercondylar fossa within seconds. The variability of the intercondylar notch in ACL-injured samples was analyzed using SSM techniques, focusing on its principal components. Additionally, gender differences in notch volume were examined using t-tests.

Results

The best deep learning method for automatic segmentation of the notch was SegResNet, which achieved a Dice similarity coefficient of over 0.88 and a Hausdorff distance of 0.73 mm. The small volume-related relative error (0.06) illustrates the goodness of the result. Three principal components accounted for 72.59% of the variation, including notch volume, shape, width, and height. Females had statistically significant smaller notch compared with males with ACL injury (P < 0.001).

Conclusion

By examining notch volume and its variability in ACL-injured patients, it is possible to understand the complex anatomy of the intercondylar notch and tailor ACL reconstructions accordingly.
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
Knee
Knee 医学-外科
CiteScore
3.80
自引率
5.30%
发文量
171
审稿时长
6 months
期刊介绍: The Knee is an international journal publishing studies on the clinical treatment and fundamental biomechanical characteristics of this joint. The aim of the journal is to provide a vehicle relevant to surgeons, biomedical engineers, imaging specialists, materials scientists, rehabilitation personnel and all those with an interest in the knee. The topics covered include, but are not limited to: • Anatomy, physiology, morphology and biochemistry; • Biomechanical studies; • Advances in the development of prosthetic, orthotic and augmentation devices; • Imaging and diagnostic techniques; • Pathology; • Trauma; • Surgery; • Rehabilitation.
×
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