- Christian Matek, Sebastian Krappe, Christian Münzenmayer, Torsten Haferlach, Carsten Marr. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set. Blood, 2021; 138 (20): 1917 DOI: 10.1182/blood.2020010568
Largest open-source database for bone marrow cell images
The Helmholtz Munich researchers developed the largest open access database on microscopic images of bone marrow cells to date. The database consists of more than 170,000 single-cell images from over 900 patients with various blood diseases. It is the result of a collaboration from Helmholtz Munich with the LMU University Hospital Munich, the MLL Munich Leukemia Lab (one of the largest diagnostic providers in this field worldwide) and Fraunhofer Institute for Integrated Circuits.
Using the database to boost artificial intelligence
“On top of our database, we have developed a neural network that outperforms previous machine learning algorithms for cell classification in terms of accuracy, but also in terms of generalizability,” says Christian Matek, lead author of the new study. The deep neural network is a machine learning concept specifically designed to process images. “The analysis of bone marrow cells has not yet been performed with such advanced neural networks,” Christian Matek explains, “which is also due to the fact that high-quality, public datasets have not been available until now.”
The researchers aim to further expand their bone marrow cell database to capture a broader range of findings and to prospectively validate their model. “The database and the model are freely available for research and training purposes — to educate professionals or as a reference for further AI-based approaches e.g. in blood cancer diagnostics,” says study leader Carsten Marr.