- produce a label matrix with output
- evaluate efficacy
- Ground truth will be stored in files named anXXXXX_YYYY_MM_DD_fov_NNNNN_rois.mat ( where all repeated capital letters are decimal digits with XXXXX indicates the animal, YYYY_MM_DD indicates the date in year month day format, and NNNNN represents a field of view ).
- These files will contain a group called
roi. Inside of that group will be the following fields.mask- An array where each index represents a different ROI. All other indices represent spatial coordinates.centroid_xy- A matrix where the first index will represent the relevant ROI. The last will represent which axis of the centroid is being represented.burst_rate- A vector where the index represents the relevant ROI.
- In addition, the raw data will be supplied in TIFFs named Image_Registration_4_anXXXXX_YYYY_MM_DD_main_FFF.tif ( where all repeated capital letters are decimal digits with XXXXX indicates the animal, YYYY_MM_DD indicates the date in year month day format, and FFF represents the frame )
Two metrics will be used.
- Distance between centroids of detected and ground truth ROIs (i.e. as measured by the L2 or Euclidean norm).
- Percentage overlap of detected and ground truth ROIs (i.e. mask intersection divided by union).
The results will be broked down into 3 categories. The determination of which category they fall into will dependent on a chosen acceptance threshold for the relevant metric.
- True Positives - How many matches are there between detected and ground truth ROIs
- False Negatives - How many ground truth ROIs are missed?
- False Positives - How many detected ROIs are incorrect (i.e. do not correspond to one in the ground truth).
After assessing both metrics, the next relevant question is how does activity/noise affect detectability? This will require plots of these categories against the burst rate.
Finally, these metrics must be stored in some readable format for future use.
Data is almost always 2 channel. Only use the green channel (1st one).