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Automatic detection of relevant regions for the morphological analysis of bone marrow slides

: Krappe, Sebastian; Leisering, Richard; Haferlach, Torsten; Wittenberg, Thomas; Münzenmayer, Christian


Tolxdorff, T.:
Bildverarbeitung für die Medizin 2016 : Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 13. bis 15. März 2016 in Berlin
Berlin: Springer Vieweg, 2016 (Informatik aktuell)
ISBN: 3-662-49464-7
ISBN: 978-3-662-49464-6 (Print)
ISBN: 978-3-662-49465-3 (Online)
Workshop Bildverarbeitung für die Medizin (BVM) <2016, Berlin>
Fraunhofer IIS ()
Visualisierung; Merkmal; medizinische BV; Hämatologie; Farbbilder; Computer Assistierte Mikroskopie

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. For the conventional cytological analysis the bone marrow aspirate smear is stained and examined by means of a light microscope. At first the cell density, the bone marrow fat content and qualitative changes of the cells are observed in a midlevel magnification. Afterwards, cells of different types are identified and counted. Especially this step is time-consuming, subjective, tedious and error-prone. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason an automation of the bone marrow analysis is pursued. In the meantime semi-automated prototypes for the automated analysis are available where the determination of relevant regions is done manually. In order to accomplish a fully automated workflow the relevant regions have to be found automatically. In this work we propose a method for the automatic determination of relevant regions which is based on a decision tree using color features. 1024 virtual slides of bone marrow smears are used for the development and evaluation of the proposed approach. For the test dataset the accuracy of the trained decision tree classifier is 99.85 %, the sensitivity is 65.88 % and the specificity is 99.98 %. Also a color coded evaluation in the virtual slides is provided. With the proposed method it is possible to detect relevant regions automatically for the automated morphological analysis for the first time. This method provides a valuable suggestion of regions to analyze in high magnification with a high accuracy.