Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Association of Kidney Volume With Patient-Reported Outcomes in ADPKD.

Kidney international reports·2026

Same author

Multimodal learning for scalable representation of high-dimensional medical data.

Frontiers in digital health·2026

Same author

Enhancing image retrieval through optimal barcode representation.

Scientific reports·2025

Same author

Patients With Mild ADPKD by Kidney Imaging but Low Estimated GFR.

Kidney international reports·2025

Same author

Characterizing the spatial patterns and determinants of cerebrospinal fluid pseudorandom flow in the human brain with low b-value diffusion MRI.

Imaging neuroscience (Cambridge, Mass.)·2025

Same author

Foundation Models for Histopathology-Fanfare or Flair.

Mayo Clinic proceedings. Digital health·2025

Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026

Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026

Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026

Same journal

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026

Same journal

Differential regional textural attributes of tongue in normal and acidity patients in the light of traditional Chinese medicine.

Computers in biology and medicine·2026

Same journal

SC-MSDNet: Spatial-consistent multi-view self-distillation for retinal OCT classification.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

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

Whole slide image representation in bone marrow cytology.

Youqing Mu¹, H R Tizhoosh², Taher Dehkharghanian³

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

Computers in Biology and Medicine

|October 14, 2023

Summary

This summary is machine-generated.

AI generates compact representations of bone marrow cytology whole slide images (WSIs) for improved hematology diagnostics. This method aids in WSI retrieval and classification, supporting AI-assisted computational pathology.

Keywords:

Cytology Deep learning Digital pathology Slide-level representations

More Related Videos

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

Published on: September 14, 2016

In Situ Exploration of Murine Megakaryopoiesis using Transmission Electron Microscopy

In Situ Exploration of Murine Megakaryopoiesis using Transmission Electron Microscopy

Published on: September 8, 2021

Related Experiment Videos

Last 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

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

High-Throughput, Multi-Image Cryohistology of Mineralized Tissues

Published on: September 14, 2016

In Situ Exploration of Murine Megakaryopoiesis using Transmission Electron Microscopy

In Situ Exploration of Murine Megakaryopoiesis using Transmission Electron Microscopy

Published on: September 8, 2021

Area of Science:

Computational pathology
Artificial intelligence in medicine
Hematopathology

Background:

Bone marrow aspirate cytology is crucial for hematology diagnoses but visual inspection is complex and expertise is limited.
AI-based computational pathology aims to create compact representations of whole slide images (WSIs) for diagnosis, with limited application in cytology.
Developing automated methods for bone marrow cytology can aid clinical decision-making and address expertise shortages.

Purpose of the Study:

To develop and evaluate a deep learning-based method for generating compact, slide-level vector representations of bone marrow aspirate WSIs.
To assess the utility of these representations for WSI retrieval and diagnostic classification tasks in hematology.

Main Methods:

Leveraged a previously developed end-to-end AI system for cell counting and classification from bone marrow aspirate WSIs.
Constructed bags of individual cell features and applied multiple instance learning to extract WSI vector representations.
Evaluated representation quality using WSI retrieval (mAP@10) and diagnostic classification (F1 score) tasks.

Main Results:

Achieved a mean Average Precision at 10 (mAP@10) of 0.58 ±0.02 for WSI retrieval, significantly outperforming the random baseline (0.39 ±0.1).
Predicted five diagnostic labels for WSIs with a weighted-average F1 score of 0.57 ±0.03 using a k-nearest-neighbors model, surpassing random guessing (0.26 ±0.02).

Conclusions:

This study presents the first trainable mechanism for generating compact, slide-level representations in bone marrow cytology using deep learning.
The developed method effectively captures semantic information in WSIs, showing potential for improved hematology diagnostics and AI-assisted computational pathology.
These compact representations can serve as a foundation for clinical decision-support tools in hematology.