Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

By Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee-Wah Tsang, Jin Tae Kwak, Nasir M. Rajpoot. Medical Image Anal., 2018

1,449 citations recorded by the source corpus.

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow. 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5986547c7380f5a8fb6028093f827b3662f838a2

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Citations in the corpus

CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, methodology): “All promising DL models [37, 40, 41, 42, 43, 8, 29, 45, 9] for nuclei instance segmentation mentioned previously are based on CNNs.”
Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation (2024, methodology): “• We demonstrate the proposed method achieve a better balance between stability and plasticity than state-of-the-art methods via extensive experiments on two famous public histopathological datasets, MoNuSAC [33] and CoNSeP [14].”
Multimodal data fusion for cancer biomarker discovery with deep learning (2023, background): “For example, selected tiles could be overlayed with Hover-Net 155 to segment and classify nuclei to evaluate predominant cell types (Fig.”
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images (2020, methodology): “The proposed DSF-CNN once again achieves the best performance compared to other methods for both binary DICE and panoptic quality, on par with the state-of-the-art HoVer-Net method, while requiring a fraction of the parameter count.”
Artificial intelligence for digital and computational pathology (2023, methodology): “In clinical settings, these regions can then be used to ensure the reliability of the method by comparing it with expert knowledge and serve as an indicator for the automatic selection of regions of interest.”