Identifying and Counting Avian Blood Cells in Whole Slide Images via Deep Learning

This dataset contains research data as presented in: Vogelbacher, M.; Strehmann, F.; Bellafkir, H.; Mühling, M.; Korfhage, N.; Schneider, D.; Rösner, S.; Schabo, D. G.; Farwig, N.; Freisleben, B. Identifying and Counting Avian Blood Cells in Whole Slide Images via Deep Learning. Submitted for publication. 2024. In this article, we present a novel approach to automatically quantify avian red and white blood cells in whole slide images. Our approach is based on two deep neural network models. The first model determines image regions that are suitable for counting blood cells, and the second model is an instance segmentation model that detects the cells in the determined image regions. The region selection model achieves up to 97.3% in terms of the F1 score, and the instance segmentation model achieves up to 90.7% in terms of mean average precision. Our approach helps ornithologists to acquire hematological data from avian blood smears more precisely and efficiently. The data published here include the raw annotated data as well as the trained models for the automated counting of blood cells in avian blood smears. Our code is publicly available at https://github.com/umr-ds/avibloodcount.