Identifying and Counting Avian Blood Cells in Whole Slide Images via Deep Learning
Abstract
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.
Metadata
Date | 2024-01-12 |
Authors | Vogelbacher,
Markus
|
Contributors | Strehmann,
Finja
Bellafkir, Hicham Mühling, Markus Korfhage, Nikolaus Schneider, Daniel Rösner, Sascha Schabo, Dana G. Farwig, Nina Freisleben, Bernd |
Relationship | Is Referenced By:
(URL)
https://github.com/umr-ds/avibloodcount
|
License | Creative Commons Attribution-NonCommercial 4.0 |
Faculty | FB12:Department of Mathematics and Computer Science |
Language | English |
Data types | Dataset Model |
Keywords | Cell segmentation Bird blood analysis Microscopy images Blood smear images Object detection Ornithology |
DFG-Subjects | 203-03 Ökologie und Biodiversität der Tiere und Ökosysteme, Organismische Interaktionen 409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung |
DDC-Numbers | 590 004 |
Funding | Hessian State Ministry for Higher Education, Research and the Arts (HMWK) (LOEWE Natur 4.0 and hessian.AI Connectom AI4Birds, AI4BirdsDemo) |
URI | https://data.uni-marburg.de/handle/dataumr/250
|
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Files
Name | Format | Size | Checksum (MD5) |
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README.txt | .txt | 3.165Kb | c3db41d8f8df19ca455bc4d30c9759a7 |
dataset_countability.tar | .tar | 2.950Gb | f0b971cc294109b7020a4953867f54c2 |
dataset_segmentation.tar | .tar | 1.929Gb | e7d5c79c7aa136b5de835b3538ef761e |
efficientNet_B0.onnx | .onnx | 15.99Mb | 0023d645390f1574ba21b8b72e2b4708 |
condInst_R101.pth | .pth | 442.6Mb | 6027ca87982b14b259860e59e5400baa |
license_CC-BY-NC-4.0.txt | .txt | 18.88Kb | d882379f6314cc023ed84088401bbde8 |
Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 4.0