Mahbod Amirreza, Schaefer Gerald, Löw Christine, Dorffner Georg, Ecker Rupert, Ellinger Isabella
Institute for Pathophysiology and Allergy Research, Medical University of Vienna, A-1090 Vienna, Austria.
Department of Computer Science, Loughborough University, Loughborough LE11 3TT, UK.
Diagnostics (Basel). 2021 May 27;11(6):967. doi: 10.3390/diagnostics11060967.
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.
细胞核实例分割可被视为计算机介导的组织学荧光染色（FS）图像分析中的一个关键点。针对这项任务已经提出了许多计算机辅助方法，其中，监督深度学习（DL）方法表现最佳。影响FS图像基于DL的细胞核实例分割性能的一个重要标准是所使用的图像位深度，但据我们所知，迄今为止尚未有研究调查这种影响。在这项工作中，我们发布了一个完全注释的FS组织学图像数据集，该数据集包含不同图像放大倍数下以及来自五个不同小鼠器官的细胞核。此外，通过不同的预处理技术并使用一种基于DL的先进方法，我们研究了图像位深度（即8位与16位）对细胞核实例分割性能的影响。从我们的数据集和另一个公开可用的数据集中获得的结果表明，对于用8位和16位图像训练的模型，细胞核实例分割性能非常有竞争力。这表明在大多数情况下，处理8位图像对于FS图像的细胞核实例分割就足够了。包含原始图像块以及相应分割掩码的数据集在已发布的GitHub存储库中公开可用。
