I am using the Interactive Arena Detection Tool to find the best parameters to fit an ROI (arena mask) around my arena and extract the mouse from our data. I found a good combination of parameters for each of my mice that gave good ROIs and a clean extraction of the mouse. For example:
However, upon extracting the data, 25% of the extracted MoSeq sessions give ROIs that do not match the test extraction's ROI. These ROIs did not fit the arena and did not give usable extractions. For example:
I double-checked that:
the session_config file was being updated with the right parameters when I click "Save session parameters"
the session_config parameter "manual_set_depth_range" is set to true
I even tried changing the config parameter "manual_set_depth_range" to true manually, but this did not resolve the issue (I have since changed that parameter back to false in the config file)
Restarting the Jupyter Notebook also did not resolve the issue. My MoSeq2 app version is v1.3.1 and I am using a Windows subsystem for Linux on Windows 10. Why are the parameters yielding different results on the test extractions and the actual extractions?
Hello,
Thank you for creating this invaluable tool!
I am using the Interactive Arena Detection Tool to find the best parameters to fit an ROI (arena mask) around my arena and extract the mouse from our data. I found a good combination of parameters for each of my mice that gave good ROIs and a clean extraction of the mouse. For example:
However, upon extracting the data, 25% of the extracted MoSeq sessions give ROIs that do not match the test extraction's ROI. These ROIs did not fit the arena and did not give usable extractions. For example:
I double-checked that:
Restarting the Jupyter Notebook also did not resolve the issue. My MoSeq2 app version is v1.3.1 and I am using a Windows subsystem for Linux on Windows 10. Why are the parameters yielding different results on the test extractions and the actual extractions?