Abstract
Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository.
Highlights
The morphology of nuclei regulates and, vice versa, is regulated by the environment, as well as the activity of the cell
We investigated the impact of image bit depth on the nuclei instance segmentation performance using our proposed dataset and a second publicly available dataset
If we disregarded these three images and recalculated the evaluation indexes, an average Dice score of 97.0 ± 1.2%, an average aggregate Jaccard index (AJI) of 87.3 ± 7.6% and an average panoptic quality (PQ) score of 86.8 ± 7.2% were obtained for 8 bit images, while for 16 bit images, an average Dice score of 97.0 ± 1.2%, an average AJI of
Summary
The morphology of nuclei regulates and, vice versa, is regulated by the environment, as well as the activity of the cell. Nuclei parameters are important measures for cell biologists investigating physiological and pathophysiological processes and are used clinically for, e.g., the diagnosis of malignant and other diseases [1]. Nuclear morphology can for instance be dramatically influenced during the differentiation of immune cells [1]. The chromatin of malignant cells can be altered, and nuclear membrane irregularities such as thickening, dents, folds, grooves and pseudo inclusions can be observed. Malignant cells may display a combination, but not necessarily all of these morphologic abnormalities [2]. Viral infections of cells can affect the morphology of the nucleus of the host cell. Among the most common morphological alterations of the nucleus observed due to viral infection of cells are the disruption of the nuclear membrane and fragmentation of the nucleus. Fluorescence imaging is widely used in cell biology and biomedical research, and the identification of the cell nucleus is an important first step in the quantitative analysis of fluorescence images [5]
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