Spatial Maturity Regression for the Classification of Hematopoietic Cells

International Conference on Image Processing Theory, Tools and Applications (IPTA), vol. 36, pp. 1–6

Abstract

In contrast to peripheral blood, cells in bone marrow microscopy images are not only characterized by the cell lineage but also a maturity stage within the lineage. As maturation is a continuous process, the differentiation between various stages falls into the category of (ordinal) regression. In this work, we propose Spatial Maturity Regression - a technique that regularizes the learning process to enforce a sensible positioning of maturity stages in the embedding space. To this end, we propose and evaluate several curve models, target definitions and loss function that incorporate this domain knowledge. We show that the classification F-scores improve up to 2.4 percentage points when enforcing regression targets along learnable curves in the embedding space. This technique further allows visualization of individual predictions by providing the projected position along the learnt curve.

Authors 7

1. Philipp Gräbel Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

2. Julian Thull Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

3. Martina Margrit Crysandt Aachen Department of Hematology, Oncology

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   University Hospital RWTH Aachen University Aachen,Department of Hematology Oncology, Hemostaseology and Stem Cell Transplantation,Germany

   Department of Hematology Oncology, Hemostaseology and Stem Cell Transplantation, University Hospital RWTH Aachen University Aachen, Germany

4. Barbara M. Klinkhammer Aachen Institute of Pathology

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen University,Aachen,Germany

   Institute of Pathology, University Hospital RWTH Aachen University, Aachen, Germany

5. Peter Boor Aachen Institute of Pathology

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   Institute of Pathology, University Hospital RWTH Aachen University,Aachen,Germany

   Institute of Pathology, University Hospital RWTH Aachen University, Aachen, Germany

6. Tim H. Brümmendorf Aachen Department of Hematology, Oncology

   RWTH Aachen University · Universitätsklinikum Aachen

   Affiliation as printed

   University Hospital RWTH Aachen University Aachen,Department of Hematology Oncology, Hemostaseology and Stem Cell Transplantation,Germany

   Department of Hematology Oncology, Hemostaseology and Stem Cell Transplantation, University Hospital RWTH Aachen University Aachen, Germany

7. Dorit Merhof Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany