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HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images


Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclear segmentation and classification enables the quantitative analysis of tens of thousands of nuclei within a whole-slide pathology image, opening up possibilities of further analysis of large-scale nuclear morphometry. However, automated nuclear segmentation and classification is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intra-class variability such as the nuclei of tumour cells. Additionally, some of the nuclei are often clustered together. To address these challenges, we present a novel convolutional neural network for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass. These distances are then utilised to separate clustered nuclei, resulting in an accurate segmentation, particularly in areas with overlapping instances. Then, for each segmented instance the network predicts the type of nucleus via a devoted up-sampling branch. We demonstrate state-of-the-art performance compared to other methods on multiple independent multi-tissue histology image datasets. As part of this work, we introduce a new dataset of Haematoxylin & Eosin stained colorectal adenocarcinoma image tiles, containing 24,319 exhaustively annotated nuclei with associated class labels.


  • A network targeted at simultaneous segmentation and classification of nuclei.
  • Introduce horizontal and vertical distance maps to separate clustered nuclei.
  • An interpretable evaluation framework that quantifies nuclear segmentation.
  • A new dataset of 24,319 exhaustively annotated nuclei with associated class labels.


Nuclear segmentation; nuclear classification; computational pathology; deep learning.


S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. "HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images." Medical Image Analysis, Sept. 2019. [doi]Link opens in a new window


Please download the dataset from this link.

Released under the Apache 2.0 license.

If you intend to publish research work that uses this dataset, you must cite the above publication.