State of the Art Cell Detection in Bone Marrow Whole Slide Images

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.

AI-driven projection tomography with multicore fibre-optic cell rotation

“…Due to the high contrast of the images after cell segmentation, we extract the exact position of the cell in each frame. This is accomplished by determining the minimal enclosing circle of the cell’s contour, a technique that has proven effective in various image analysis contexts 48 . As demonstrated in Fig.…”