Role of Image Acquisition and Patient Phenotype Variations in Automatic
  Segmentation Model Generalization
Timothy L. Kline Sumana Ramanathan Harrison C. Gottlich Panagiotis Korfiatis Adriana V. Gregory
Purpose: This study evaluated the out-of-domain performance and
generalization capabilities of automated medical image segmentation models,
with a particular focus on adaptation to new image acquisitions and disease
type.
  Materials: Datasets from both non-contrast and contrast-enhanced abdominal CT
scans of healthy patients and those with polycystic kidney disease (PKD) were
used. A total of 400 images (100 non-contrast controls, 100 contrast controls,
100 non-contrast PKD, 100 contrast PKD) were utilized for training/validation
of models to segment kidneys, livers, and spleens, and the final models were
then tested on 100 non-contrast CT images of patients affected by PKD.
Performance was evaluated using Dice, Jaccard, TPR, and Precision.
  Results: Models trained on a diverse range of data showed no worse
performance than models trained exclusively on in-domain data when tested on
in-domain data. For instance, the Dice similarity of the model trained on 25%
from each dataset was found to be non-inferior to the model trained purely on
in-domain data.
  Conclusions: The results indicate that broader training examples
significantly enhances model generalization and out-of-domain performance,
thereby improving automated segmentation tools' applicability in clinical
settings. The study's findings provide a roadmap for future research to adopt a
data-centric approach in medical image AI model development.