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multi-tissue histology images

Multi-tissue histology images are microscopic digital pathology images that capture biological tissue specimens across a diverse range of anatomical sites, organ types, or distinct histologic tissue compartments. Typically prepared using standard chemical stains such as hematoxylin and eosin to highlight cellular and extracellular components, these images display broad morphological heterogeneity, varying nuclear structures, and differing background textures inherent to distinct biological environments. In computational pathology and biomedical research, multi-tissue histology imagery is widely used to develop and benchmark automated image analysis algorithms, such as nuclear segmentation, cell classification, and tissue phenotyping, ensuring that computational models can reliably generalize across disparate organ systems and staining variations rather than remaining specialized to a single tissue type.

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

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

Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, Nasir Rajpoot

OrganizationsCentre for Evolution and CancerDepartment of Computer ScienceDepartment of Computer Science and EngineeringDepartment of PathologyDivision of Molecular PathologyMathematics for Real-World Systems Centre for Doctoral TrainingSejong UniversityThe Institute of Cancer ResearchUniversity Hospitals Coventry and WarwickshireUniversity of Warwick

Why you should read this

Presents HoVer-Net, a deep learning architecture that uses horizontal and vertical pixel distance maps to accurately separate clustered nuclei and simultaneously classify cell types across multi-tissue histology images.

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 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.

Added

2026-09-25