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Fixation and Sectioning

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

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Updated: Jul 13, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

在骨髓细胞学中整体幻灯片图像表示.

Youqing Mu¹, H R Tizhoosh², Taher Dehkharghanian³

¹University of Toronto, Toronto, Canada; McMaster University, Hamilton, Canada.

Computers in biology and medicine

|October 14, 2023

概括

人工智能生成骨髓细胞学整片图像 (WSIs) 的紧表示,以改善血液学诊断. 该方法有助于WSI检索和分类,支持人工智能辅助的计算病理学.

关键词:

细胞学细胞学深度学习是一种深度学习.数字病理学数字病理学幻灯片级别的表示.

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Last Updated: Jul 13, 2025

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Published on: April 8, 2015

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Published on: September 14, 2016

In Situ Exploration of Murine Megakaryopoiesis using Transmission Electron Microscopy

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Published on: September 8, 2021

科学领域:

计算病理学计算病理学
医学中的人工智能
血液病理学血液病理学

背景情况:

骨髓吸附细胞学对于血液学诊断至关重要,但视觉检查复杂,专业知识有限.
基于人工智能的计算病理学旨在创建用于诊断的整个幻灯片图像 (WSIs) 的紧表示,在细胞学中应用有限.
开发用于骨髓细胞学的自动化方法可以帮助临床决策和解决专业知识短缺问题.

研究的目的:

开发和评估一种基于深度学习的方法,用于生成骨髓吸收WSIs的紧的幻灯片级向量表示.
评估这些表示对于血液学中的WSI检索和诊断分类任务的有用性.

主要方法:

利用先前开发的端到端人工智能系统来从骨髓吸入物WSI中进行细胞计数和分类.
构建了单个细胞特征的袋子,并应用了多个实例学习来提取WSI矢量表示.
使用WSI检索 (mAP@10) 和诊断分类 (F1评分) 任务评估表示质量.

主要成果:

在10 (mAP@10) 获得WSI检索0.58 ±0.02的平均平均精度,显著超过随机基线 (0.39 ±0.1).
预测了WSI的五个诊断标签,使用k-近邻模型的加权平均F1得分为0.57 ±0.03,超过随机猜测 (0.26 ±0.02).

结论:

这项研究提出了第一个可训练的机制,用于在骨髓细胞学中使用深度学习生成紧的幻灯片级表示.
开发的方法有效地捕捉了WSIs中的语义信息,显示了改善血液学诊断和人工智能辅助计算病理学的潜力.
这些紧的表示可以作为血液学临床决策支持工具的基础.