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.

通过使用卷积神经网络将核数据分离和分类，进而对数字病理学的工作流程进行优化。