ACE Segmentation Function

Cutting edge vision transformer and CNN-based DL architectures trained on very large LSFM datasets to map cFos brain-wide.

CLI

To look at the arguments that need to be provided to the function, invoke the help menu using:

$ miracl seg ace -h

The following menu will be printed to the terminal:

usage: miracl ace [-h] -sai SA_INPUT_FOLDER -sao SA_OUTPUT_FOLDER -sam
                  {unet,unetr,ensemble} [-sas height width depth]
                  [-sar X-res Y-res Z-res] [-saw SA_NR_WORKERS]
                  [-sac SA_CACHE_RATE] [-sasw SA_SW_BATCH_SIZE] [-samc] [-sav]
                  [-sau]

AI-based Cartography of Ensembles (ACE) segmentation method

optional arguments:
  -h, --help            show this help message and exit
  -sai SA_INPUT_FOLDER, --sa_input_folder SA_INPUT_FOLDER
                        path to raw tif/tiff data folder
  -sao SA_OUTPUT_FOLDER, --sa_output_folder SA_OUTPUT_FOLDER
                        path to output file folder
  -sam {unet,unetr,ensemble}, --sa_model_type {unet,unetr,ensemble}
                        model architecture
  -sas height width depth, --sa_image_size height width depth
                        image size (type: int; default: fetched from image
                        header)
  -sar X-res Y-res Z-res, --sa_resolution X-res Y-res Z-res
                        voxel size (type: _validate_vox_res)
  -saw SA_NR_WORKERS, --sa_nr_workers SA_NR_WORKERS
                        number of cpu cores deployed to pre-process image
                        patches in parallel (type: int; default: 4)
  -sac SA_CACHE_RATE, --sa_cache_rate SA_CACHE_RATE
                        percentage of raw data that is loaded into cpu during
                        segmentation (type: float; default: 0.0)
  -sasw SA_SW_BATCH_SIZE, --sa_sw_batch_size SA_SW_BATCH_SIZE
                        number of image patches being processed by the model
                        in parallel on gpu (type: int; default: 4)
  -samc, --sa_monte_dropout
                        use Monte Carlo dropout (default: False)
  -sav, --sa_visualize_results
                        visualizing model output after predictions (default:
                        False)
  -sau, --sa_uncertainty_map
                        enable map (default: False)

Flag	Parameter	Type	Description	Default
-sai, --sa_input_folder	SA_INPUT_FOLDER	str	path to raw tif/tiff data folder	None (required)
-sao, --sa_output_folder	SA_OUTPUT_FOLDER	str	path to output file folder	None (required)
-sam, --sa_model_type	{unet,unetr,ensemble}	str	model architecture	None (required)
-sas, --sa_image_size	height width depth	int	image size; provided as three arguments	fetched from image header
-sar, --sa_resolution	X-res Y-res Z-res	int	voxel resolution; provided as three arguments	None (required)
-saw, --sa_nr_workers	SA_NR_WORKERS	int	number of cpu cores deployed to pre-process image patches in parallel	4
-sac, --sa_cache_rate	SA_CACHE_RATE	float	percentage of raw data that is loaded into cpu during segmentation	0.0
-sasw,--sa_sw_batch_size	SA_SW_BATCH_SIZE	int	number of image patches being processed by the model in parallel on gpu	4
-samc,--sa_monte_dropout	True/False	bool	use Monte Carlo dropout	False
-sav, --sa_visualize_results	True/False	bool	visualizing model output after predictions	False
-sau, --sa_uncertainty_map	True/False	bool	enable map	False

Note

The -sa in the flag part stands for segmentation ACE.

Example usage:

$ miracl seg ace \
   -sai ./walking/subject_01/cells/ \
   -sao ./output_dir \
   -sam unet