Comparing image normalization techniques in an end-to-end model for automated modic changes classification from MRI images

IF 1.9 Q3 CLINICAL NEUROLOGY
Andrea Cina , Daniel Haschtmann , Dimitrios Damopoulos , Nicolas Gerber , Markus Loibl , Tamas Fekete , Frank Kleinstück , Fabio Galbusera
{"title":"Comparing image normalization techniques in an end-to-end model for automated modic changes classification from MRI images","authors":"Andrea Cina ,&nbsp;Daniel Haschtmann ,&nbsp;Dimitrios Damopoulos ,&nbsp;Nicolas Gerber ,&nbsp;Markus Loibl ,&nbsp;Tamas Fekete ,&nbsp;Frank Kleinstück ,&nbsp;Fabio Galbusera","doi":"10.1016/j.bas.2023.102738","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>Modic Changes (MCs) are MRI alterations in spine vertebrae's signal intensity. This study introduces an end-to-end model to automatically detect and classify MCs in lumbar MRIs. The model's two-step process involves locating intervertebral regions and then categorizing MC types (MC0, MC1, MC2) using paired T1-and T2-weighted images. This approach offers a promising solution for efficient and standardized MC assessment.</p></div><div><h3>Research question</h3><p>The aim is to investigate how different MRI normalization techniques affect MCs classification and how the model can be used in a clinical setting.</p></div><div><h3>Material and methods</h3><p>A combination of Faster R–CNN and a 3D Convolutional Neural Network (CNN) is employed. The model first identifies intervertebral regions and then classifies MC types (MC0, MC1, MC2) using paired T1-and T2-weighted lumbar MRIs. Two datasets are used for model development and evaluation.</p></div><div><h3>Results</h3><p>The detection model achieves high accuracy in identifying intervertebral areas, with Intersection over Union (IoU) values above 0.7, indicating strong localization alignment. Confidence scores above 0.9 demonstrate the model's accurate levels identification. In the classification task, standardization proves the best performances for MC type assessment, achieving mean sensitivities of 0.83 for MC0, 0.85 for MC1, and 0.78 for MC2, along with balanced accuracy of 0.80 and F1 score of 0.88.</p></div><div><h3>Discussion and conclusion</h3><p>The study's end-to-end model shows promise in automating MC assessment, contributing to standardized diagnostics and treatment planning. Limitations include dataset size, class imbalance, and lack of external validation. Future research should focus on external validation, refining model generalization, and improving clinical applicability.</p></div>","PeriodicalId":72443,"journal":{"name":"Brain & spine","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772529423010263/pdfft?md5=5c4b4b0fbead3bbed18f403c823c0e95&pid=1-s2.0-S2772529423010263-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain & spine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772529423010263","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction

Modic Changes (MCs) are MRI alterations in spine vertebrae's signal intensity. This study introduces an end-to-end model to automatically detect and classify MCs in lumbar MRIs. The model's two-step process involves locating intervertebral regions and then categorizing MC types (MC0, MC1, MC2) using paired T1-and T2-weighted images. This approach offers a promising solution for efficient and standardized MC assessment.

Research question

The aim is to investigate how different MRI normalization techniques affect MCs classification and how the model can be used in a clinical setting.

Material and methods

A combination of Faster R–CNN and a 3D Convolutional Neural Network (CNN) is employed. The model first identifies intervertebral regions and then classifies MC types (MC0, MC1, MC2) using paired T1-and T2-weighted lumbar MRIs. Two datasets are used for model development and evaluation.

Results

The detection model achieves high accuracy in identifying intervertebral areas, with Intersection over Union (IoU) values above 0.7, indicating strong localization alignment. Confidence scores above 0.9 demonstrate the model's accurate levels identification. In the classification task, standardization proves the best performances for MC type assessment, achieving mean sensitivities of 0.83 for MC0, 0.85 for MC1, and 0.78 for MC2, along with balanced accuracy of 0.80 and F1 score of 0.88.

Discussion and conclusion

The study's end-to-end model shows promise in automating MC assessment, contributing to standardized diagnostics and treatment planning. Limitations include dataset size, class imbalance, and lack of external validation. Future research should focus on external validation, refining model generalization, and improving clinical applicability.

比较端到端模型中的图像归一化技术,实现磁共振成像模态变化自动分类
导言Modic Changes(MCs)是脊柱椎体信号强度的 MRI 改变。本研究介绍了一种端到端模型,用于自动检测和分类腰椎磁共振成像中的 MCs。该模型的两个步骤包括定位椎间区域,然后使用成对的 T1 和 T2 加权图像对 MC 类型(MC0、MC1、MC2)进行分类。这种方法为高效、标准化的 MC 评估提供了一种很有前景的解决方案。研究问题旨在研究不同的 MRI 归一化技术对 MC 分类的影响,以及该模型在临床环境中的应用。该模型首先识别椎间区域,然后使用成对的 T1 和 T2 加权腰椎 MRI 图像对 MC 类型(MC0、MC1、MC2)进行分类。结果该检测模型在识别椎间区域方面达到了很高的准确度,其 "交集大于联合"(Intersection over Union,IoU)值高于 0.7,表明定位对齐度很高。置信度得分超过 0.9,表明该模型能够准确识别椎间区。在分类任务中,标准化证明了 MC 类型评估的最佳性能,MC0 的平均灵敏度为 0.83,MC1 为 0.85,MC2 为 0.78,平衡准确度为 0.80,F1 得分为 0.88。不足之处包括数据集的大小、类别不平衡以及缺乏外部验证。未来的研究应侧重于外部验证、完善模型的通用性和提高临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Brain & spine
Brain & spine Surgery
CiteScore
1.10
自引率
0.00%
发文量
0
审稿时长
71 days
×
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