通过深度学习实现组织病理学冷冻切片的超分辨率,保留组织结构

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-07-08 DOI:10.1002/aisy.202300672
Elad Yoshai, Gil Goldinger, Miki Haifler, Natan T. Shaked
{"title":"通过深度学习实现组织病理学冷冻切片的超分辨率,保留组织结构","authors":"Elad Yoshai,&nbsp;Gil Goldinger,&nbsp;Miki Haifler,&nbsp;Natan T. Shaked","doi":"10.1002/aisy.202300672","DOIUrl":null,"url":null,"abstract":"<p>Histopathology plays a pivotal role in medical diagnostics. In contrast to preparing permanent sections for histopathology, a time-consuming process, preparing frozen sections is significantly faster and can be performed during surgery, where the sample scanning time should be optimized. Super-resolution techniques allow imaging of histopathalogical samples in lower magnification, thus sparing scanning time. Herein, a new approach is presented to super-resolution of histopathological frozen sections, with focus on achieving better distortion measures, rather than pursuing photorealistic images that may compromise critical diagnostic information. Our deep-learning architecture focuses on learning the error between interpolated images and real images; thereby generating high-resolution images while preserving critical image details, which reduces the risk of diagnostic misinterpretation. This is done by leveraging the loss functions in the frequency domain and assigning higher weights to the reconstruction of complex, high-frequency components. In comparison with existing methods, significant improvements are obtained in terms of distortion metrics, improving the pathologist's clinical decisions. This approach has a great potential to provide faster frozen-section imaging, with less scanning, speeding up intraoperative decisions, while preserving the high-resolution details in the imaged sample.</p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"6 8","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202300672","citationCount":"0","resultStr":"{\"title\":\"Super-Resolution of Histopathological Frozen Sections via Deep Learning Preserving Tissue Structure\",\"authors\":\"Elad Yoshai,&nbsp;Gil Goldinger,&nbsp;Miki Haifler,&nbsp;Natan T. Shaked\",\"doi\":\"10.1002/aisy.202300672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Histopathology plays a pivotal role in medical diagnostics. In contrast to preparing permanent sections for histopathology, a time-consuming process, preparing frozen sections is significantly faster and can be performed during surgery, where the sample scanning time should be optimized. Super-resolution techniques allow imaging of histopathalogical samples in lower magnification, thus sparing scanning time. Herein, a new approach is presented to super-resolution of histopathological frozen sections, with focus on achieving better distortion measures, rather than pursuing photorealistic images that may compromise critical diagnostic information. Our deep-learning architecture focuses on learning the error between interpolated images and real images; thereby generating high-resolution images while preserving critical image details, which reduces the risk of diagnostic misinterpretation. This is done by leveraging the loss functions in the frequency domain and assigning higher weights to the reconstruction of complex, high-frequency components. In comparison with existing methods, significant improvements are obtained in terms of distortion metrics, improving the pathologist's clinical decisions. This approach has a great potential to provide faster frozen-section imaging, with less scanning, speeding up intraoperative decisions, while preserving the high-resolution details in the imaged sample.</p>\",\"PeriodicalId\":93858,\"journal\":{\"name\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"volume\":\"6 8\",\"pages\":\"\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202300672\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202300672\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202300672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

组织病理学在医学诊断中起着举足轻重的作用。与制作组织病理学永久切片这一耗时的过程相比,制作冷冻切片要快得多,而且可以在手术过程中进行,从而优化样本扫描时间。超分辨率技术能以较低的放大率对组织病理学样本进行成像,从而节省扫描时间。本文提出了一种组织病理学冰冻切片超分辨率的新方法,重点是实现更好的失真测量,而不是追求可能会损害关键诊断信息的逼真图像。我们的深度学习架构侧重于学习插值图像与真实图像之间的误差,从而在生成高分辨率图像的同时保留关键图像细节,降低诊断误读的风险。这是通过利用频域中的损失函数并为复杂的高频成分重建分配更高的权重来实现的。与现有方法相比,该方法在失真指标方面取得了显著改善,从而提高了病理学家的临床决策水平。这种方法在提供更快的冷冻切片成像、减少扫描次数、加快术中决策、同时保留成像样本的高分辨率细节方面具有巨大潜力。
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

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
Super-Resolution of Histopathological Frozen Sections via Deep Learning Preserving Tissue Structure

Histopathology plays a pivotal role in medical diagnostics. In contrast to preparing permanent sections for histopathology, a time-consuming process, preparing frozen sections is significantly faster and can be performed during surgery, where the sample scanning time should be optimized. Super-resolution techniques allow imaging of histopathalogical samples in lower magnification, thus sparing scanning time. Herein, a new approach is presented to super-resolution of histopathological frozen sections, with focus on achieving better distortion measures, rather than pursuing photorealistic images that may compromise critical diagnostic information. Our deep-learning architecture focuses on learning the error between interpolated images and real images; thereby generating high-resolution images while preserving critical image details, which reduces the risk of diagnostic misinterpretation. This is done by leveraging the loss functions in the frequency domain and assigning higher weights to the reconstruction of complex, high-frequency components. In comparison with existing methods, significant improvements are obtained in terms of distortion metrics, improving the pathologist's clinical decisions. This approach has a great potential to provide faster frozen-section imaging, with less scanning, speeding up intraoperative decisions, while preserving the high-resolution details in the imaged sample.

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
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
4 weeks
期刊最新文献
Masthead A Flexible, Architected Soft Robotic Actuator for Motorized Extensional Motion Design and Optimization of a Magnetic Field Generator for Magnetic Particle Imaging with Soft Magnetic Materials High-Performance Textile-Based Capacitive Strain Sensors via Enhanced Vapor Phase Polymerization of Pyrrole and Their Application to Machine Learning-Assisted Hand Gesture Recognition Optimized Magnetically Docked Ingestible Capsules for Non-Invasive Refilling of Implantable Devices
×
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