ErrorSim: a deep learning error simulator in left ventricle segmentations

• Bruno F. Raquel USP
• Matheus A. O. Ribeiro USP
• Fátima L. S. Nunes USP

DOI: https://doi.org/10.5753/eniac.2025.13909

Abstract

Left ventricle segmentation in Cardiac Magnetic Resonance exams is important for medical diagnosis. Deep learning methods have excelled in obtaining segmentations similar to those of experts. However, one of the current limitations is the arbitrary production of anatomical errors that can compromise the diagnosis. Given this problem, this work proposes a framework for categorization and controlled automatic simulation of different anatomical errors. The framework favors the development of methods aimed at detecting and correcting these errors. Results indicate that the proposed framework is capable of generating errors of several categories and replicating the same errors produced by networks (Dice > 0.8).

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