Cell Instance Segmentation Using Z-Stacks In Digital Cytology

This work addresses cell instance segmentation in digital cytology slides. Automated segmentation of cells is an essential step toward automated cell analysis and pathology diagnosis, however cell instance segmentation remains a challenging task, especially for touching or overlapping cells. This work first introduces a new large dataset of overlapping urothelial cell Z-stacks, allowing the comparison and validation of modern algorithms for cell instance segmentation, improving previously proposed methods by a large margin. In addition, a modified backbone architecture is proposed to directly use Z-stacks as inputs, relieving from the necessity of using Extended Depth of Field projections of the volumes. Such backbone allows to reduce the total prediction time per sample by a factor up to 8, with no drop in segmentation performances. Dataset and Code will be made available.