Bots for Software-Assisted Analysis of Image-Based Transcriptomics

Marcelo Cicconet, Daniel R. Hochbaum, David L. Richmond, Bernardo L. Sabatin
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
We introduce software assistants -bots -for the task of analyzing image-based transcriptomic data. The key steps in this process are detecting nuclei, and counting associated puncta corresponding to labeled RNA. Our main release offers two algorithms for nuclei segmentation, and two for spot detection, to handle data of different complexities. For challenging nuclei segmentation cases, we enable the user to train a stacked Random Forest, which includes novel circularity features that leverage
more » ... ior knowledge regarding nuclei shape for better instance segmentation. This machine learning model can be trained on a modern CPUonly computer, yet performs comparably with respect to a more hardware-demanding state-of-the-art deep learning approach, as demonstrated through experiments. While the primary motivation for the bots was image-based transcriptomics, we also demonstrate their applicability to the more general problem of scoring "spots" in nuclei.
doi:10.1109/iccvw.2017.24 dblp:conf/iccvw/CicconetHRS17 fatcat:5nwixsujbffhhfugzt2kelolbm