Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells

August 27, 2019 · Entered Twilight · 🏛 International Conference on Medical Image Computing and Computer-Assisted Intervention

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Repo contents: .gitignore, README.md, open_files.sh, run_cross_weights.py, run_denseMIL.py, run_ilc.py, run_ilc_1ch.py, run_ilc_cross_weight.py, run_ilc_inferno.py, run_model.py, run_responces.py, service_scripts.py, src, submit_job.py, validate_model.py

Authors Artem Lukoyanov, Isabella Haberbosch, Constantin Pape, Alwin Kraemer, Yannick Schwab, Anna Kreshuk arXiv ID 1908.10109 Category cs.CV: Computer Vision Citations 0 Venue International Conference on Medical Image Computing and Computer-Assisted Intervention Repository https://github.com/kreshuklab/centriole_detection Last Checked 1 month ago

Abstract

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.