Comparison of Otsu and an adapted Chan-Vese method to determine thyroid active volume using Monte Carlo generated SPECT images.

IF 3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Jonas Högberg, Christoffer Andersén, Tobias Rydén, Jakob H Lagerlöf
{"title":"Comparison of Otsu and an adapted Chan-Vese method to determine thyroid active volume using Monte Carlo generated SPECT images.","authors":"Jonas Högberg, Christoffer Andersén, Tobias Rydén, Jakob H Lagerlöf","doi":"10.1186/s40658-023-00609-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The Otsu method and the Chan-Vese model are two methods proven to perform well in determining volumes of different organs and specific tissue fractions. This study aimed to compare the performance of the two methods regarding segmentation of active thyroid gland volumes, reflecting different clinical settings by varying the parameters: gland size, gland activity concentration, background activity concentration and gland activity concentration heterogeneity.</p><p><strong>Methods: </strong>A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu's threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient.</p><p><strong>Results: </strong>Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]).</p><p><strong>Conclusions: </strong>The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.</p>","PeriodicalId":11559,"journal":{"name":"EJNMMI Physics","volume":"11 1","pages":"6"},"PeriodicalIF":3.0000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774246/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EJNMMI Physics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40658-023-00609-9","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The Otsu method and the Chan-Vese model are two methods proven to perform well in determining volumes of different organs and specific tissue fractions. This study aimed to compare the performance of the two methods regarding segmentation of active thyroid gland volumes, reflecting different clinical settings by varying the parameters: gland size, gland activity concentration, background activity concentration and gland activity concentration heterogeneity.

Methods: A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu's threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient.

Results: Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]).

Conclusions: The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.

使用蒙特卡洛生成的 SPECT 图像测定甲状腺有效容积的 Otsu 方法和改良的 Chan-Vese 方法的比较。
背景:大津法和陈-维斯模型是两种被证明在确定不同器官和特定组织部分体积方面性能良好的方法。本研究旨在通过改变腺体大小、腺体活动浓度、背景活动浓度和腺体活动浓度异质性等参数,比较这两种方法在分割甲状腺活动体积方面的性能,以反映不同的临床环境:方法:对体积分别为 20、35 和 50 毫升的三个橡皮泥甲状腺模型进行计算机断层扫描。根据 Hounsfield 值将图像数据分为橡皮泥和水。用蒙特卡洛法模拟了 60 个同位素锝-99 m([计算公式:见正文]Tc)的单光子发射计算机断层扫描(SPECT)投影。对 SPECT 图像进行线性组合,产生了 12 种不同的体积和背景组合:每种组合既有均匀的甲状腺活性浓度,又有三个不同相对活性浓度的热点(共 48 幅 SPECT 图像)。选择的相对背景水平分别为模型活动浓度的 5%、10%、15% 和 20%,热点活动浓度分别为 100%(均质情况)、150%、200% 和 250%。在使用基于蒙特卡洛的 SPECT 重建算法 Sahlgrenska 学院重建代码(SARec)进行重建之前,加入了泊松噪声(在 20% 的背景水平下变异系数为 0.8,不包括散射)。应用了两种不同的分割算法:对结果进行了评估,涉及每个甲状腺体积的相对体积、平均绝对误差和标准偏差,以及骰子相似系数:结果:两种方法都能很好地分割图像,与真实体积的偏差相似。它们似乎略微高估了小体积,低估了大体积。不同的背景水平对两种方法的影响也类似。然而,Chan-Vese 模型的偏差较小,配对 t 检验显示骰子相似系数的分布之间存在显著差异(p 值[公式:见正文]):研究表明,Chan-Vese 模型的性能更好,也更稳健,但在实施和临床使用上更具挑战性。在性能和用户友好性之间需要权衡。
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
EJNMMI Physics
EJNMMI Physics Physics and Astronomy-Radiation
CiteScore
6.70
自引率
10.00%
发文量
78
审稿时长
13 weeks
期刊介绍: EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.
×
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