NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

ABSTRACT: 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 pro

SUBMITTER: Mahbod A 

PROVIDER: S-EPMC10940572 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature