English

ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network

Image and Video Processing 2022-03-01 v1 Computer Vision and Pattern Recognition

Abstract

Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making. The Colon Nuclei Identification and Counting Challenge 2022 (CoNIC Challenge 2022) supports the development and comparability of segmentation and classification methods for histopathological images. In this contribution, we describe our CoNIC Challenge 2022 method ciscNet to segment, classify and count cell nuclei, and report preliminary evaluation results. Our code is available at https://git.scc.kit.edu/ciscnet/ciscnet-conic-2022.

Keywords

Cite

@article{arxiv.2202.13960,
  title  = {ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network},
  author = {Moritz Böhland and Oliver Neumann and Marcel P. Schilling and Markus Reischl and Ralf Mikut and Katharina Löffler and Tim Scherr},
  journal= {arXiv preprint arXiv:2202.13960},
  year   = {2022}
}

Comments

CoNIC Challenge 2022 submission

R2 v1 2026-06-24T09:56:42.207Z