Running adpkd_segmentation/inference/inference.py
generates:
adpkd-segmentation/saved_inference
saved_inference/adpkd-segmentation/{PATIENT-ID}/{SERIES}
accession
or time
to prevent conflicts..dcm
files are the raw input dicom files.json
files contains all attributes for each image required for calculating TKV{
"patient": {Patient-id},
"seq": "Axial T2 SS-FSE",
"min_image_int16": -15,
"max_image_int16": 1983,
"kidney_pixels": null,
"vox_vol": 16.71142578125,
"dim": [256, 256],
"transform_rsize_dim": [640, 640]
}
img.npy
files are the dicom files converted to numpy for inputlogit.npy
files are model logits after inferencepred.npy
files are model predictions after applying prediction function (i.e. sigmoid)/saved_inference/adpkd-segmentation/patient_identifier/series_name
├── IM1_attrib.json
├── IM1.dcm
├── IM1_img.npy
├── IM1_logit.npy
├── IM1_pred.npy
├── IM2_attrib.json
├── IM2.dcm
├── IM2_img.npy
├── IM2_logit.npy
├── IM2_pred.npy
├── IM3_attrib.json
├── IM3.dcm
├── IM3_img.npy
├── IM3_logit.npy
├── IM3_pred.npy
....