I am a dedicated lecturer (first mission) and researcher (second mission) and with my work aim at contributing to the three main cornerstones of the academic system of the MedUni Vienna that are research, education and patient care. I am convinced of the importance of third mission activities; thus, I persue an intense exchange on science with people of all ages. As a member of two female scientists networks (WISIA, WNWmed), I am also a passionate advocate of empowerment of women in science. Overall, my activities align to UN sustainable development goals (SDGs) 3 and 4 and thus shall 1) ensure healthy lives and promote well-being for all at all ages and 2) foster lifelong learning for all.

Finally, I hope that we can make our Institute and University more sustainable. Thus, we initiated Green IPA

• First Mission l Teaching
  • Medical University of Vienna
  • Other Universities

• Second Mission l Research
  • Studying placental functions under physiologic and pathologic conditions
  • Advancing automated microscopy
  • Providing IFM-expertise in collaborations

• Third Mission l Science Education and Scientific Communication

   

  NEWS Archive

CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images

Mahbod A, Schaefer G, Bancher B, Löw Ch, Dorffner G, Ecker R, Ellinger I

Computers in Biology and Medicine. Available online 22 March 2021, 104349

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.