  The Ki-67 proliferation index is an essential biomarker that helps
pathologists to diagnose and select appropriate treatments. However, automatic
evaluation of Ki-67 is difficult due to nuclei overlapping and complex
variations in their properties. This paper proposes an integrated pipeline for
accurate automatic counting of Ki-67, where the impact of nuclei separation
techniques is highlighted. First, semantic segmentation is performed by
combining the Squeez and Excitation Resnet and Unet algorithms to extract
nuclei from the background. The extracted nuclei are then divided into
overlapped and non-overlapped regions based on eight geometric and statistical
features. A marker-based Watershed algorithm is subsequently proposed and
applied only to the overlapped regions to separate nuclei. Finally, deep
features are extracted from each nucleus patch using Resnet18 and classified
into positive or negative by a random forest classifier. The proposed
pipeline's performance is validated on a dataset from the Department of
Pathology at H\^opital Nord Franche-Comt\'e hospital.
