  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, namely formalin-fixed
paraffin-embedded samples (FFPE) and frozen tissue samples (FS). Although
FFPE-derived H&E stained tissue sections are the most widely used samples, H&E
staining on frozen sections derived from FS samples is a relevant method in
intra-operative surgical sessions as it can be performed fast. Due to
differences in the protocols 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 variability. Moreover, we
investigate the effects of tissue fixation/embedding protocol (i.e., FS or
FFPE) on the automatic nuclei instance segmentation performance of one of the
state-of-the-art DL approaches. We also create 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.
