CURVAS: Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation

In medical imaging, DL models are often tasked with delineating structures or abnormalities within complex anatomical structures, such as tumors, blood vessels, or organs. Uncertainty arises from the inherent complexity and variability of these structures, leading to challenges in precisely defining their boundaries. This uncertainty is further compounded by interrater variability, as different medical experts may have varying opinions on where the true boundaries lie. DL models must grapple with these discrepancies, leading to inconsistencies in segmentation results across different annotators and potentially impacting diagnosis and treatment decisions. Addressing interrater variability in DL for medical segmentation involves the development of robust algorithms capable of capturing and quantifying uncertainty, as well as standardizing annotation practices and promoting collaboration among medical experts to reduce variability and improve the reliability of DL-based medical image analysis. Interrater variability poses significant challenges in the field of DL for medical image segmentation.
 
Furthermore, achieving model calibration, a fundamental aspect of reliable predictions, becomes notably challenging when dealing with multiple classes and raters. Calibration is pivotal for ensuring that predicted probabilities align with the true likelihood of events, enhancing the model's reliability. It must be considered that, even if not clearly, having multiple classes account for uncertainties arising from their interactions. Morevoer, incorporating annotations from multiple raters adds another layer of complexity, as differing expert opinions may contribute to a broader spectrum of variability and computational complexity.
 
Consequently, the development of robust algorithms capable of effectively capture and quantify variability and uncertainty, while also accommodating the nuances of multi-class and multi-rater scenarios, becomes imperative. Striking a balance between model calibration, accurate segmentation and handling variability in medical annotations is crucial for the success and reliability of DL-based medical image analysis.
 
The data used for this challenge consists of abdominal CTs in which pancreas, liver and kidneys are labeled. Each CT will be labeled by three radiologists.