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
Digital pathology enables the acquisition, management and sharing of information retrieved from stained digitised tissue sections from patient-derived biopsies in a digital environment
The main aim of this set of experiments is to investigate the effect of tissue fixation/embedding protocol on the performance of the nuclei segmentation model
A statistical test comparing the results derived from the combined MoNuSeg-frozen tissue samples (FS)/MoNuSeg-fixed paraffin-embedded samples (FFPE) dataset gives pvalues of 0.0196, 0.0001, and 0.0001 for Dice score, aggregate Jaccard index (AJI), and panoptic quality (PQ) score, respectively, when comparing to the results from MoNuSeg-FS, while p-values of 0.0068 (Dice), 0.0005 (AJI), and 0.0001 (PQ) are obtained when comparing to the results derived from the MoNuSeg-FFPE dataset
Summary
Digital pathology enables the acquisition, management and sharing of information retrieved from stained digitised tissue sections from patient-derived biopsies in a digital environment This offers many benefits including image interpretation by remotely located specialists or further use of the samples for scientific purposes [1]. Examination of Hematoxylin and Eosin (H&E)-stained tissue sections can reveal important information about individual cells and their functional status [4] Judgement of these histopathological images remains the “gold standard” in diagnosing a variety of diseases including almost all types of cancer. Shape, type, count, and density are the key components in the evaluation process of H&E-stained tissue images To extract these features automatically with a computer-based method, nuclei instance segmentation is required [5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
