NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images
In computational pathology, automatic nuclei instance segmentation plays an
essential role in whole slide image analysis. While many computerized
approaches have been proposed for this task, supervised deep learning (DL)
methods have shown superior segmentation performances compared to classical
machine learning and image processing techniques. However, these models need
fully annotated datasets for training which is challenging to acquire,
especially in the medical domain. In this work, we release one of the biggest
fully manually annotated datasets of nuclei in Hematoxylin and Eosin
(H&E)-stained histological images, called NuInsSeg. This dataset contains 665
image patches with more than 30,000 manually segmented nuclei from 31 human and
mouse organs. Moreover, for the first time, we provide additional ambiguous
area masks for the entire dataset. These vague areas represent the parts of the
images where precise and deterministic manual annotations are impossible, even
for human experts. The dataset and detailed step-by-step instructions to
generate related segmentation masks are publicly available at
https://www.kaggle.com/datasets/ipateam/nuinsseg and
https://github.com/masih4/NuInsSeg, respectively.