CURVAS 2nd Edition¶
Pancreatic Adenocarcinoma Vascular Invasion (PDACVI)¶
New version of the training dataset has been published in Zenodo: https://zenodo.org/records/15720085.
In this version, the STAPLE consensus label has been added, and orientation inconsistencies have been corrected. All CT scans are now standardized to head-first orientation.
Visit our website: https://sycaimedical.com/curvaspdacvi/
In medical imaging, Deep Learning (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.
This challenge is designed to promote awareness of the impact uncertainty has on clinical applications of medical image analysis. In our last-year edition, we proposed a competition based on modeling the uncertainty of segmenting three abdominal organs, namely kidney, liver and pancreas, focusing on organ volume as a clinical quantity of interest. This year, we go one step further and propose to segment pancreatic pathological structures, namely Pancreatic Ductal Adenocarcinoma (PDAC), with the clinical goal of understanding vascular involvement, a key measure of tumor resectability. In this above context, uncertainty quantification is a much more challenging task, given the wildly varying contours that different PDAC instances show.
This year, we will provide a richer dataset, in which we start from an already existing dataset of clinically verified contrast-enhanced abdominal CT scans with a single set of manual annotations (provided by the PANORAMA organization), and make an effort to construct four extra manual annotations per PDAC case. In this way, we will assemble a unique dataset that creates a notable opportunity to analyze the impact of multi-rater annotations in several dimensions, e.g. different annotation protocols or different annotator experiences, to name a few.
Winners¶
Top five performing methods will be announced publicly. The CURVAS consortium will extend invitations to the top the corresponding teams to join its ranks. These teams will earn recognition as consortium authors in an upcoming influential journal publication that will contain the contributions from this challenge.
Furthermore, winners will be invited to present their methods and results in the challenge event hosted in MICCAI 2024.
Finally, there will be cash prices for the top three methods:
¶
The participating teams may publish their own results separately only after the organizer has published a challenge paper and always mentioning the organizer's challenge paper.
This work was supported by the Catalan Government inside the program ”Doctorats Industrials” and by the company Sycai Technologies SL. Mertixell Riera i Marín is supported by the industrial doctorate of the AGAUR 2021-063.
The challenge has been co-funded by Proyectos de Colaboración Público-Privada (CPP2021-008364), funded by MCIN/AEI, and the European Union through the NextGenerationEU/PRTR.
