Deep learning reduced pediatric craniospinal target contouring from eight hours to 30 minutes
Automated pediatric CSI contours achieved a mean Dice score of 0.959 and reduced target-delineation time from approximately eight hours to 30 minutes.
Tag
auto-segmentation
T1-weighted MRI plus CT best automated EPTN neuro-oncology organ contouring
A dual-input MRI–CT model segmented 25 neurological organs with a median Dice score of 0.80 and surface Dice of 0.84.
PocketNet matched nnU-Net segmentation with 40-fold lower model storage
PocketNet matched nnU-Net accuracy for cervical MRI segmentation while using 40-fold less storage and approximately half the training time.
High geometric agreement did not guarantee physician acceptance of AI contours
Thoracic AI contours showed strong geometric and dosimetric agreement, but physicians still rejected some heart and oesophageal contours.
Deep learning reduced head and neck auto-segmentation editing, but user variability persisted
Deep learning auto-segmentation reduced editing, but both minimal and excessive manual edits were associated with lower clinical acceptability.