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Purpose: This study evaluated the out-of-domain performance andgeneralization capabilities of automated medical image segmentation models,with a particular focus on adaptation to new image acquisitions and diseasetype.  Materials: Datasets from both non-contrast and contrast-enhanced abdominal CTscans of healthy patients and those with polycystic kidney disease (PKD) wereused. A total of 400 images (100 non-contrast controls, 100 contrast controls,100 non-contrast PKD, 100 contrast PKD) were utilized for training/validationof models to segment kidneys, livers, and spleens, and the final models werethen 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 worseperformance than models trained exclusively on in-domain data when tested onin-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 onin-domain data.  Conclusions: The results indicate that broader training examplessignificantly enhances model generalization and out-of-domain performance,thereby improving automated segmentation tools' applicability in clinicalsettings. The study's findings provide a roadmap for future research to adopt adata-centric approach in medical image AI model development.

Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization