bibtext: @InProceedings{ Yan_CoarseGrained_MICCAI2024,
                   author = { Yan, Yige and Cheng, Jun and Yang, Xulei and Gu, Zaiwang and Leng, Shuang and Tan, Ru San and Zhong, Liang and Rajapakse, Jagath C. },
                   title = { { Coarse-Grained Mask Regularization for Microvascular Obstruction Identification from non-contrast Cardiac Magnetic Resonance } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: Identification of microvascular obstruction (MVO) in acute myocardial infarction patients is critical for prognosis and has a direct link to mortality risk. Current approaches using late gadolinium enhancement (LGE) for contrast-enhanced cardiac magnetic resonance (CMR) pose risks to the kidney and may not be applicable to many patients. This highlights the need to explore alternative non-contrast imaging methods, such as cine CMR, for MVO identification. However, the scarcity of datasets and the challenges in annotation make the MVO identification in cine CMR challenging and remain largely under-explored. For this purpose, we propose a non-contrast MVO identification framework in cine CMR with a novel coarse-grained mask regularization strategy to better utilize information from LGE annotations in training. We train and test our model on a dataset comprising 680 cases. Our model demonstrates superior performance over competing methods in cine CMR-based MVO identification, proving its feasibility and presenting a novel and patient-friendly approach to the field. The code is available at https://github.com/code-koukai/MVO-identification.
title: Coarse-Grained Mask Regularization for Microvascular Obstruction Identification from non-contrast Cardiac Magnetic Resonance
authors: Yan, Yige Cheng, Jun Yang, Xulei Gu, Zaiwang Leng, Shuang Tan, Ru San Zhong, Liang Rajapakse, Jagath C.