State of the art cell detection in bone marrow whole slide images

Gräbel P, Özkan Ö, Crysandt M, Herwartz R, Baumann M, Klinkhammer B, Boor P, Brümmendorf T, Merhof D (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Journal of Pathology Informatics Medknow Publications

Book Volume: 12

Article Number: 326215

Journal Issue: 1

DOI: 10.4103/jpi.jpi_71_20

Abstract

Context: Diseases of the hematopoietic system such as leukemia is diagnosed using bone marrow samples. The cell type distribution plays a major role but requires manual analysis of different cell types in microscopy images. Aims: Automated analysis of bone marrow samples requires detection and classification of different cell types. In this work, we propose and compare algorithms for cell localization, which is a key component in automated bone marrow analysis. Settings and Design: We research fully supervised detection architectures but also propose and evaluate several techniques utilizing weak annotations in a segmentation network. We further incorporate typical cell-like artifacts into our analysis. Whole slide microscopy images are acquired from the human bone marrow samples and annotated by expert hematologists. Subjects and Methods: We adapt and evaluate state-of-the-art detection networks. We further propose to utilize the popular U-Net for cell detection by applying suitable preprocessing steps to the annotations. Statistical Analysis Used: Evaluations are performed on a held-out dataset using multiple metrics based on the two different matching algorithms. Results: The results show that the detection of cells in hematopoietic images using state-of-the-art detection networks yields very accurate results. U-Net-based methods are able to slightly improve detection results using adequate preprocessing - despite artifacts and weak annotations. Conclusions: In this work, we propose, U-Net-based cell detection methods and compare with state-of-the-art detection methods for the localization of hematopoietic cells in high-resolution bone marrow images. We show that even with weak annotations and cell-like artifacts, cells can be localized with high precision.

Involved external institutions

Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Universitätsklinikum Aachen (UKA) DE Germany (DE)

How to cite

APA:

Gräbel, P., Özkan, Ö., Crysandt, M., Herwartz, R., Baumann, M., Klinkhammer, B.,... Merhof, D. (2021). State of the art cell detection in bone marrow whole slide images. Journal of Pathology Informatics, 12(1). https://doi.org/10.4103/jpi.jpi_71_20

MLA:

Gräbel, Philipp, et al. "State of the art cell detection in bone marrow whole slide images." Journal of Pathology Informatics 12.1 (2021).

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