  When a pre-trained general auto-segmentation model is deployed at a new
institution, a support framework in the proposed Prior-guided DDL network will
learn the systematic difference between the model predictions and the final
contours revised and approved by clinicians for an initial group of patients.
The learned style feature differences are concatenated with the new patients
(query) features and then decoded to get the style-adapted segmentations. The
model is independent of practice styles and anatomical structures. It
meta-learns with simulated style differences and does not need to be exposed to
any real clinical stylized structures during training. Once trained on the
simulated data, it can be deployed for clinical use to adapt to new practice
styles and new anatomical structures without further training.
  To show the proof of concept, we tested the Prior-guided DDL network on six
different practice style variations for three different anatomical structures.
Pre-trained segmentation models were adapted from post-operative clinical
target volume (CTV) segmentation to segment CTVstyle1, CTVstyle2, and
CTVstyle3, from parotid gland segmentation to segment Parotidsuperficial, and
from rectum segmentation to segment Rectumsuperior and Rectumposterior. The
mode performance was quantified with Dice Similarity Coefficient (DSC). With
adaptation based on only the first three patients, the average DSCs were
improved from 78.6, 71.9, 63.0, 52.2, 46.3 and 69.6 to 84.4, 77.8, 73.0, 77.8,
70.5, 68.1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial,
Rectumsuperior, and Rectumposterior, respectively, showing the great potential
of the Priorguided DDL network for a fast and effortless adaptation to new
practice styles
