  Despite considerable strides in developing deep learning models for 3D
medical image segmentation, the challenge of effectively generalizing across
diverse image distributions persists. While domain generalization is
acknowledged as vital for robust application in clinical settings, the
challenges stemming from training with a limited Field of View (FOV) remain
unaddressed. This limitation leads to false predictions when applied to body
regions beyond the FOV of the training data. In response to this problem, we
propose a novel loss function that penalizes predictions in implausible body
regions, applicable in both single-dataset and multi-dataset training schemes.
It is realized with a Body Part Regression model that generates axial slice
positional scores. Through comprehensive evaluation using a test set featuring
varying FOVs, our approach demonstrates remarkable improvements in
generalization capabilities. It effectively mitigates false positive tumor
predictions up to 85% and significantly enhances overall segmentation
performance.
