  Deep learning (DL) models for medical image segmentation are highly
influenced by intensity variations of input images and lack generalization due
to primarily utilizing pixels' intensity information for inference. Acquiring
sufficient training data is another challenge limiting models' applications. We
proposed to leverage the consistency of organs' anatomical shape and position
information in medical images. We introduced a framework leveraging recurring
anatomical patterns through global binary masks for organ segmentation. Two
scenarios were studied.1) Global binary masks were the only model's (i.e.
U-Net) input, forcing exclusively encoding organs' position and shape
information for segmentation/localization.2) Global binary masks were
incorporated as an additional channel functioning as position/shape clues to
mitigate training data scarcity. Two datasets of the brain and heart CT images
with their ground-truth were split into (26:10:10) and (12:3:5) for training,
validation, and test respectively. Training exclusively on global binary masks
led to Dice scores of 0.77(0.06) and 0.85(0.04), with the average Euclidian
distance of 3.12(1.43)mm and 2.5(0.93)mm relative to the center of mass of the
ground truth for the brain and heart structures respectively. The outcomes
indicate that a surprising degree of position and shape information is encoded
through global binary masks. Incorporating global binary masks led to
significantly higher accuracy relative to the model trained on only CT images
in small subsets of training data; the performance improved by 4.3-125.3% and
1.3-48.1% for 1-8 training cases of the brain and heart datasets respectively.
The findings imply the advantages of utilizing global binary masks for building
generalizable models and to compensate for training data scarcity.
