改进距离正则化水平集进化的脑室分割方法。

4区 计算机科学 Q1 Arts and Humanities Visual Computing for Industry, Biomedicine, and Art Pub Date : 2020-12-07 DOI:10.1186/s42492-020-00064-8
Thirumagal Jayaraman, Sravan Reddy M, Manjunatha Mahadevappa, Anup Sadhu, Pranab Kumar Dutta
{"title":"改进距离正则化水平集进化的脑室分割方法。","authors":"Thirumagal Jayaraman,&nbsp;Sravan Reddy M,&nbsp;Manjunatha Mahadevappa,&nbsp;Anup Sadhu,&nbsp;Pranab Kumar Dutta","doi":"10.1186/s42492-020-00064-8","DOIUrl":null,"url":null,"abstract":"<p><p>Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%-90%, specificity in the range of 98%-99%, and accuracy in the range of 95%-98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.</p>","PeriodicalId":52384,"journal":{"name":"Visual Computing for Industry, Biomedicine, and Art","volume":"3 1","pages":"29"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719594/pdf/","citationCount":"2","resultStr":"{\"title\":\"Modified distance regularized level set evolution for brain ventricles segmentation.\",\"authors\":\"Thirumagal Jayaraman,&nbsp;Sravan Reddy M,&nbsp;Manjunatha Mahadevappa,&nbsp;Anup Sadhu,&nbsp;Pranab Kumar Dutta\",\"doi\":\"10.1186/s42492-020-00064-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%-90%, specificity in the range of 98%-99%, and accuracy in the range of 95%-98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.</p>\",\"PeriodicalId\":52384,\"journal\":{\"name\":\"Visual Computing for Industry, Biomedicine, and Art\",\"volume\":\"3 1\",\"pages\":\"29\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719594/pdf/\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Visual Computing for Industry, Biomedicine, and Art\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1186/s42492-020-00064-8\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Arts and Humanities\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Computing for Industry, Biomedicine, and Art","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1186/s42492-020-00064-8","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 2

摘要

神经退行性疾病通常以神经元丧失引起的脑萎缩为特征。脑室是大脑中最重要的结构之一;它们的形状会改变,因为它们的内容物,脑脊液。分析脑室的形态学变化,有助于诊断萎缩,因为感兴趣的区域需要从背景中分离出来。提出了一种结合区域强度信息的改进距离正则化水平集进化分割方法。该方法用于从磁共振成像和计算机断层成像的正常和萎缩受试者的脑图像中分割脑室。结果表明,该方法的灵敏度为65%-90%,特异度为98%-99%,准确度为95%-98%。峰值信噪比和结构相似指数也作为确定分割精度的性能指标:分别为95%和0.95。对于不同的数据集,水平集公式的参数是不同的。采用优化程序对参数进行微调。结果表明,该方法对噪声图像具有较好的鲁棒性和有效性。该方法具有自适应和多模态的特点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Modified distance regularized level set evolution for brain ventricles segmentation.

Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%-90%, specificity in the range of 98%-99%, and accuracy in the range of 95%-98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

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
Visual Computing for Industry, Biomedicine, and Art
Visual Computing for Industry, Biomedicine, and Art Arts and Humanities-Visual Arts and Performing Arts
CiteScore
5.60
自引率
0.00%
发文量
28
审稿时长
5 weeks
期刊最新文献
Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18. Hyperparameter optimization for cardiovascular disease data-driven prognostic system. Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images. Vision transformer architecture and applications in digital health: a tutorial and survey. DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1