The Library
Hover-Net : simultaneous segmentation and classification of nuclei in multi-tissue histology images
Tools
Tools
Tools
Graham, Simon, Vu, Quoc Dang, Raza, Shan E. Ahmed, Azam, Ayesha, Tsang, Yee Wah, Kwak, Jin Tae and Rajpoot, Nasir M. (Nasir Mahmood) (2019) Hover-Net : simultaneous segmentation and classification of nuclei in multi-tissue histology images. Medical Image Analysis, 58 . 101563. doi:10.1016/j.media.2019.101563 ISSN 1361-8415.
|
PDF
WRAP-HoVer-Net-simultaneous-segmentation-classification-images-Graham-2019.pdf - Accepted Version - Requires a PDF viewer. Download (12Mb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.media.2019.101563
Abstract
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.
| Item Type: | Journal Article | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > Q Science (General) Q Science > QM Human anatomy R Medicine > RB Pathology T Technology > TA Engineering (General). Civil engineering (General) |
|||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Mathematics |
|||||||||||||||
| Library of Congress Subject Headings (LCSH): | Pathology -- Data processing, Machine learning, Image processing -- Digital techniques, Histology -- Imaging | |||||||||||||||
| Journal or Publication Title: | Medical Image Analysis | |||||||||||||||
| Publisher: | Elsevier Science BV | |||||||||||||||
| ISSN: | 1361-8415 | |||||||||||||||
| Official Date: | December 2019 | |||||||||||||||
| Dates: |
|
|||||||||||||||
| Volume: | 58 | |||||||||||||||
| Article Number: | 101563 | |||||||||||||||
| DOI: | 10.1016/j.media.2019.101563 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| Date of first compliant deposit: | 25 September 2019 | |||||||||||||||
| Date of first compliant Open Access: | 18 September 2020 | |||||||||||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
