Image Processing in Haematology

 

Motivation

Blood cells are formed in the bone marrow and, as mature blood cells, later perform numerous important tasks. In diseases such as leukaemia, typical patterns change so that fewer cells mature fully in the bone marrow and more precursor or intermediate stages are found. Automated recognition of a cell's type and the evaluation of an entire bone marrow smear with regard to the number and distribution of particular cell types would speed up diagnosis, make it more objective and open up new avenues in cancer research.

Scientific Questions

The central task is to determine the distribution of cell types in the bone marrow. This requires reliable cell detection and classification. Besides the large number of artefacts and cell types, other typical problems of medical image processing also apply to this project: the images are extremely diverse and at the same time the amount of annotated data is limited.

Thesis Topics

New theses in the area of image processing in haematology are advertised regularly. In addition to the Overview , there are also numerous topics that have not yet been advertised, which will be gladly presented in a personal conversation.

Partners

• Dr. med. Martina Crysandt, Department of Haematology, Oncology, Haemostaseology and Stem Cell Transplantation, RWTH Aachen University Hospital
• Prof. Dr. med. Tim H. Brümmendorf, Department of Haematology, Oncology, Haemostaseology and Stem Cell Transplantation, RWTH Aachen University Hospital
• Prof. Dr. med. Peter Boor, Institute of Pathology, RWTH Aachen University Hospital
• Dr. rer. nat. Barbara Mara Klinkhammer, Institute of Pathology, RWTH Aachen University Hospital

Contact

Publications

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  2022inproceedings

  Philipp Gräbel, Julian Thull, Martina Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Automatic Embedding Interventions for the Classification of Hematopoietic Cells

  In: 26th International Conference on Pattern Recognition (ICPR)

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  2022inproceedings

  Philipp Gräbel, Julian Thull, Martina Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Spatial Maturity Regression for the Classification of Hematopoietic Cells

  In: IEEE International Conference on Image Processing Theory and Tools and Applications (IPTA)

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  2022inproceedings

  Philipp Gräbel, Julian Thull, Martina Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Analysis of automatically generated embedding guides for cell classification

  In: IEEE International Conference on Image Processing Theory and Tools and Applications (IPTA)

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  2022inproceedings

  Philipp Gräbel, Martina Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Ordinal classification and regression techniques for distinguishing neutrophilic cell maturity stages in human bone marrow

  In: International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI)

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  2021article

  Philipp Gräbel, Özcan Özkan, Martina Crysandt, Reinhild Herwartz, Melanie Bauman, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  State of the Art Cell Detection in Bone Marrow Slide Images

  In: Journal of Pathology Informatics 12 (1), S. 36

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  2021inproceedings

  Philipp Gräbel, Martina Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Guided Representation Learning for the Classification of Hematopoietic Cells

  In: ICCV Workshop on Computational Challenges in Digital Pathology (CDPath)

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  2021inproceedings

  Philipp Gräbel, Ina Laube, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Surrounding Cell Suppression for Unsupervised Representation Learning in Hematological Cell Classification

  In: IEEE International Symposium on Biomedical Imaging (ISBI)

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  2021inproceedings

  Philipp Gräbel, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Reduction of stain variability in bone marrow microscopy images

  In: Bildverarbeitung fuer die Medizin (BVM)

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  2021inproceedings

  Philipp Gräbel, Ina Laube, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Rotation invariance for unsupervised cell representation learning

  In: Bildverarbeitung fuer die Medizin (BVM)

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  2020inproceedings

  Philipp Gräbel, Gregor Nickel, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Systematic Analysis and Automated Search of Hyper-parameters for Cell Classifier Training

  In: IEEE International Symposium on Biomedical Imaging (ISBI)

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  2020inproceedings

  Philipp Gräbel, Özcan Özkan, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Circular Anchors for the Detection of Hematopoietic Cells using RetinaNet

  In: IEEE International Symposium on Biomedical Imaging (ISBI)

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  2019inproceedings

  Philipp Gräbel, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Refinement of Weak Annotations for the Segmentation of Bone Marrow Leukocytes

  In: 2nd MICCAI Workshop on Computational Pathology (COMPAY)

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  2018inproceedings

  Philipp Gräbel, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf and Dorit Merhof

  Evaluating Out-of-the-box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow

  In: 1st MICCAI Workshop on Computational Pathology (COMPAY)

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