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Can I run Segmentation in CAT12 for a smaller batch of participants (instead of all participants at once)?

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I was running Segmentation in CAT12 for my 397 participants but unfortunately, the system crashed around ~280 participants. I am wondering if I can just restart Segmentation for the remaining ~120 participants separately, or do I have to rerun Segmentation for all 397 participants again? I am asking because I am unsure if the preprocessing steps in Segmentation requires all participants to be included for reference/template creation/etc. Would appreciate some clarification on this!

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Sai Pavan
Sai Pavan il 25 Mar 2024
Modificato: Sai Pavan il 25 Mar 2024
Hello Arjun,
I understand that you want to know whether one can restart segmentation in CAT12 for a smaller batch of participants after a crash without needing to rerun it for all participants again. Segmentation and other preprocessing steps are typically performed on a per-subject basis in CAT12 which means that each participant's data is processed independently of the others. Therefore, if your system crashed after processing ~280 participants, you should be able to restart the segmentation process for just the remaining ~120 participants.
Please refer to the below workflow to continue processing the remaining participants:
  • Identify the unprocessed participants by preparing a list of which participants have been successfully processed and which ones have not by checking the output files in the directories of the processed participants.
  • Prepare the data for the remaining participants in the expected directory structure.
  • Run CAT12 segmentation for the remaining participants by starting a new CAT12 batch job in MATLAB for just these participants.
Also, consider running a smaller number of participants in each batch if system resources are limited, which might help prevent future crashes.
Hope it helps!
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Arjun
Arjun il 25 Mar 2024
This is exactly what I did to solve the issue. Thank you very much confirming and clarifying this in such detail. It is very much appreciated!

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