MIMoSA

The MIMoSA pipeline integrates an automated technique for white matter lesion segmentation, developed by Dr. Alessandra Valcarcel. It provides processed T1-weighted and T2-FLAIR images, as well as a white matter lesion mask. (The current pipeline uses a pre-trained MIMoSA model, which was trained using 3T T1-weighted and T2-FLAIR images as the input).

Usage

This pipeline processes raw T1 and T2-FLAIR images to segment white matter lesions. By default, it runs bias correction, HD-BET skullstripping, registration to FLAIR space, WhiteStripe normalization, and MIMoSA.

Because MIMoSA, ACVS, and APRL follow similar preprocessing steps, their outputs are stored in the same directories. To avoid overwriting files when running any combination of these pipelines on the same data, each pipeline will create only files that were not generated by a previously run pipeline.

This pipeline can be run with or without a container. For containerized usage, Singularity can be used on a cluster or Docker locally. This pipeline can be run in individual or batch mode, meaning you can specify a certain subject and session or run the pipeline for all subjects in the folder, respectively.

These examples will run the pipeline in batch mode on the cluster. To run individually or locally/with a container, set --mode individual, or -c local/-c singularity/-c docker, respectively.

Required flags:

[-m or --mainpath]{style="font-family:menlo; color:black; font-size:16px;"}: path to parent data folder
[-t or --t1]{style="font-family:menlo; color:black; font-size:16px;"}: T1 sequence name
[-f or --flair]{style="font-family:menlo; color:black; font-size:16px;"}: FLAIR sequence name
[--toolpath]{style="font-family:menlo; color:black; font-size:16px;"}: path to pipeline folder

Other flags:

[-p or --participant]{style="font-family:menlo; color:black; font-size:16px;"}: participant ID (only needed for individual mode)
[--ses]{style="font-family:menlo; color:black; font-size:16px;"}: session ID (only needed for individual mode)
[--t2]{style="font-family:menlo; color:black; font-size:16px;"}: T2 sequence name
[-n or --n4]{style="font-family:menlo; color:black; font-size:16px;"}: run N4 bias correction. Default is TRUE
[-s or --skullstripping]{style="font-family:menlo; color:black; font-size:16px;"}: run skullstripping. Default is TRUE
[--stype]{style="font-family:menlo; color:black; font-size:16px;"}: skullstripping method: hdbet, fslbet. Default is hdbet
[-r or --registration]{style="font-family:menlo; color:black; font-size:16px;"}: run registration. Default is TRUE
[-w or --whitestripe]{style="font-family:menlo; color:black; font-size:16px;"}: run WhiteStripe normalization. Default is TRUE
[--threshold]{style="font-family:menlo; color:black; font-size:16px;"}: threshold for generating mimosa mask. Default is 0.2
[--mode]{style="font-family:menlo; color:black; font-size:16px;"}: run pipeline individually or batch. Default is batch
[-c or --container]{style="font-family:menlo; color:black; font-size:16px;"}: which container to use: singularity, docker, local, cluster. Default is cluster
[--sinpath]{style="font-family:menlo; color:black; font-size:16px;"}: path to singularity image (only needed if using singularity container - don't need to specify if using takim cluster)
[--dockerpath]{style="font-family:menlo; color:black; font-size:16px;"}: path to docker image (only needed if using docker container)
[-h or --help]{style="font-family:menlo; color:black; font-size:16px;"}: show help message

bash /path/to/PennSIVE_neuro_pip/pipelines/mimosa/code/bash/mimosa.sh -m /path/to/data -t "*T1w*.nii.gz" -f "*FLAIR*.nii.gz" --toolpath /path/to/PennSIVE_neuro_pip