StanfordMIMI / Comp2Comp

Computed tomography to body composition (Comp2Comp).
Apache License 2.0
59 stars 11 forks source link

get_dicom_or_nifti_paths_and_num naming #93

Open wasserth opened 1 year ago

wasserth commented 1 year ago

Hi, I just tried to run Comp2Comp and I experienced the following error:

from comp2comp.io.io_utils import get_dicom_nifti_paths_and_num
ImportError: cannot import name 'get_dicom_nifti_paths_and_num' from 'comp2comp.io.io_utils' (/home/jakob/dev/Comp2Comp/comp2comp/io/io_utils.py)

I fixed it by renaming get_dicom_nifti_paths_and_num to get_dicom_or_nifti_paths_and_num.

louisblankemeier commented 1 year ago

Hi Jakob,

First of all, thanks for all your work on TotalSegmentator. Please let me know if you have any ideas for Comp2Comp, which as you know, builds on TotalSegmentator.

Sorry for the error! I think we had different names in different branches that we merged in yesterday and that caused the issue. We need to add some unit tests to make sure this kind of thing doesn't happen.

Best regards, Louis

wasserth commented 1 year ago

I tried the contrast_phase classifier and it worked great for a few cases I tried it on. I thought about building something similar, but never found time to do so. The runtime was quite long for me. I think there could be potential to reduce it. Probably a lower number of radiomics features would also work fine without reducing Accuracy too much. You could check which ones are slow and see if you can remove those. I always use pyradiomics for calculating features. Maybe that is faster. But I did not check that.

louisblankemeier commented 1 year ago

Connecting @edreisMD who developed this pipeline

edreisMD commented 1 year ago

Thank you Louis. Hi @wasserth. Glad it was useful for you, thank you so much for the thoughts. We just fixed a bug that was happening in some cases, but this shouldn't reduce the runtime yet.