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
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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。

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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.

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CiteScore
1.30
自引率
0.00%
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审稿时长
4 weeks
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