Name
Abstract | ||
|---|---|---|
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 prior knowledge regarding nuclei shape for better instance segmentation. This machine learning model can be trained on a modern CPU-only 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 transcrip-tomics, we also demonstrate their applicability to the more general problem of scoring "spots" in nuclei. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICCVW.2017.24 | 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) |
Keywords | Field | DocType |
bots,software-assisted analysis,software assistants,transcriptomic data,labeled RNA,main release,spot detection,stacked Random Forest,nuclei shape,instance segmentation,machine learning model,modern CPU-only computer,deep learning approach,spots,image-based transcriptomics,nuclei segmentation cases | Computer science,Segmentation,Image based,Software,Artificial intelligence,Bioinformatics,Deep learning,Random forest,Nuclei segmentation,Machine learning | Conference |
Volume | Issue | ISSN |
2017 | 1 | 2473-9936 |
ISBN | Citations | PageRank |
978-1-5386-1035-0 | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcelo Cicconet | 1 | 28 | 7.08 |
| Daniel R. Hochbaum | 2 | 0 | 0.34 |
| david l richmond | 3 | 19 | 3.19 |
| Bernardo L. Sabatin | 4 | 0 | 0.34 |