  Background: Cell segmentation in bright-field histological slides is a
crucial topic in medical image analysis. Having access to accurate segmentation
allows researchers to examine the relationship between cellular morphology and
clinical observations. Unfortunately, most segmentation methods known today are
limited to nuclei and cannot segmentate the cytoplasm.
  Material & Methods: We present a new network architecture Cyto R-CNN that is
able to accurately segment whole cells (with both the nucleus and the
cytoplasm) in bright-field images. We also present a new dataset CytoNuke,
consisting of multiple thousand manual annotations of head and neck squamous
cell carcinoma cells. Utilizing this dataset, we compared the performance of
Cyto R-CNN to other popular cell segmentation algorithms, including QuPath's
built-in algorithm, StarDist and Cellpose. To evaluate segmentation
performance, we calculated AP50, AP75 and measured 17 morphological and
staining-related features for all detected cells. We compared these
measurements to the gold standard of manual segmentation using the
Kolmogorov-Smirnov test.
  Results: Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in
whole-cell segmentation, outperforming all other methods (QuPath
$19.46/0.91\%$; StarDist $45.33/2.32\%$; Cellpose $31.85/5.61\%$). Cell
features derived from Cyto R-CNN showed the best agreement to the gold standard
($\bar{D} = 0.15$) outperforming QuPath ($\bar{D} = 0.22$), StarDist ($\bar{D}
= 0.25$) and Cellpose ($\bar{D} = 0.23$).
  Conclusion: Our newly proposed Cyto R-CNN architecture outperforms current
algorithms in whole-cell segmentation while providing more reliable cell
measurements than any other model. This could improve digital pathology
workflows, potentially leading to improved diagnosis. Moreover, our published
dataset can be used to develop further models in the future.
