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CryoNuSeg: a dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images

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posted on 04.05.2021, 13:51 by Amirreza Mahbod, Gerald SchaeferGerald Schaefer, Benjamin Bancher, Christine Löw, Georg Dorffner, Rupert Ecker, Isabella Ellinger
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.

Funding

Austrian Research Promotion Agency (FFG), No.872636

Kaggle open data research grant

NVIDIA

History

School

  • Science

Department

  • Computer Science

Published in

Computers in Biology and Medicine

Volume

132

Publisher

Elsevier

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

16/03/2021

Publication date

2021-03-22

Copyright date

2021

ISSN

0010-4825

eISSN

1879-0534

Language

en

Depositor

Dr Gerald Schaefer. Deposit date: 4 May 2021

Article number

104349