Automated bone marrow cytology using deep learning to generate a histogram of cell types.

ABSTRACT:

Background

Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies.

Methods

We develop an end-to-end deep learning-based system for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our system rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a representation called

SUBMITTER: Tayebi RM 

PROVIDER: S-EPMC9053230 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature