  Model calibration measures the agreement between the predicted probability
estimates and the true correctness likelihood. Proper model calibration is
vital for high-risk applications. Unfortunately, modern deep neural networks
are poorly calibrated, compromising trustworthiness and reliability. Medical
image segmentation particularly suffers from this due to the natural
uncertainty of tissue boundaries. This is exasperated by their loss functions,
which favor overconfidence in the majority classes. We address these challenges
with DOMINO, a domain-aware model calibration method that leverages the
semantic confusability and hierarchical similarity between class labels. Our
experiments demonstrate that our DOMINO-calibrated deep neural networks
outperform non-calibrated models and state-of-the-art morphometric methods in
head image segmentation. Our results show that our method can consistently
achieve better calibration, higher accuracy, and faster inference times than
these methods, especially on rarer classes. This performance is attributed to
our domain-aware regularization to inform semantic model calibration. These
findings show the importance of semantic ties between class labels in building
confidence in deep learning models. The framework has the potential to improve
the trustworthiness and reliability of generic medical image segmentation
models. The code for this article is available at:
https://github.com/lab-smile/DOMINO.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning