  Tumor cells with two nuclei (binucleated cells, BiNC) or more nuclei
(multinucleated cells, MuNC) indicate an increased amount of cellular genetic
material which is thought to facilitate oncogenesis, tumor progression and
treatment resistance. In canine cutaneous mast cell tumors (ccMCT),
binucleation and multinucleation are parameters used in cytologic and
histologic grading schemes (respectively) which correlate with poor patient
outcome. For this study, we created the first open source data-set with 19,983
annotations of BiNC and 1,416 annotations of MuNC in 32 histological whole
slide images of ccMCT. Labels were created by a pathologist and an
algorithmic-aided labeling approach with expert review of each generated
candidate. A state-of-the-art deep learning-based model yielded an $F_1$ score
of 0.675 for BiNC and 0.623 for MuNC on 11 test whole slide images. In regions
of interest ($2.37 mm^2$) extracted from these test images, 6 pathologists had
an object detection performance between 0.270 - 0.526 for BiNC and 0.316 -
0.622 for MuNC, while our model archived an $F_1$ score of 0.667 for BiNC and
0.685 for MuNC. This open dataset can facilitate development of automated image
analysis for this task and may thereby help to promote standardization of this
facet of histologic tumor prognostication.
