CryoNuSeg: A dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images. (May 2021)

• Record Type: Journal Article
• Authors: Mahbod, Amirreza; Schaefer, Gerald; Bancher, Benjamin; Löw, Christine; Dorffner, Georg; Ecker, Rupert; Ellinger, Isabella
• Is Part Of: Computers in biology and medicine. Volume 132(2021)
• Publication Date: 2021-05
• Subjects: Medical image analysis -- Computational pathology -- Frozen tissue samples -- H&E staining -- Tissue fixation/embedding -- Nuclei segmentation -- Deep learning -- Benchmarking; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104349
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Nuclei instance segmentation plays an important role in the analysis of hematoxylin and eosin (H&E)-stained images. While supervised deep learning (DL)-based approaches represent the state-of-the-art in automatic nuclei instance segmentation, annotated datasets are required to train these models. There are two main types of tissue processing protocols resulting in formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS), respectively. Although FFPE-derived H&E stained tissue sections are the most widely used samples, H&E staining of frozen sections derived from FS samples is a relevant method in intra-operative surgical sessions as it can be performed more rapidly. Due to differences in the preparation of these two types of samples, the derived images and in particular the nuclei appearance may be different in the acquired whole slide images. Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality. In this paper, we introduce CryoNuSeg, the first fully annotated FS-derived cryosectioned and H&E-stained nuclei instance segmentation dataset. The dataset contains images from 10 human organs that were not exploited in other publicly available datasets, and is provided with three manual mark-ups to allow measuring intra-observer and inter-observer variabilities. Moreover, we investigate the effects of tissue fixation/embedding protocol (i.e., FS or FFPE) on the automatic nuclei instance segmentation performance and provide a baseline segmentation benchmark for the dataset that can be used in future research. A step-by-step guide to generate the dataset as well as the full dataset and other detailed information are made available to fellow researchers at https://github.com/masih4/CryoNuSeg . Highlights: The first fully annotated H&E-stained frozen tissue section dataset for nuclei instance segmentation is introduced. Three manual mark-ups are provided to measure intra-observer and inter-observer variability. The effects tissue fixation/embedding protocols on automatic nuclei instance segmentation performance are investigated. A baseline segmentation benchmark for the introduced dataset is provided