  Recent advances in high-throughput electron microscopy imaging enable
detailed study of centrosome aberrations in cancer cells. While the image
acquisition in such pipelines is automated, manual detection of centrioles is
still necessary to select cells for re-imaging at higher magnification. In this
contribution we propose an algorithm which performs this step automatically and
with high accuracy. From the image labels produced by human experts and a 3D
model of a centriole we construct an additional training set with patch-level
labels. A two-level DenseNet is trained on the hybrid training data with
synthetic patches and real images, achieving much better results on real
patient data than training only at the image-level. The code can be found at
https://github.com/kreshuklab/centriole_detection.

Computer Vision and Pattern Recognition