Key result
LVPSegNet deep learning outperforms state-of-the-art models for LV and pathology segmentation in LGE-CMR images.
Why the study?
Segmentation of the left ventricle, myocardial infarction, and microvascular obstruction from LGE-CMR images is crucial due to links with LV dysfunction, but is challenged by scarce datasets, variable morphology, class imbalance, and overlapping intensities.
Does LVPSegNet improve segmentation of the left ventricle and its pathologies in LGE-CMR images compared to state-of-the-art models?
Does LVPSegNet improve segmentation of the left ventricle and its pathologies in LGE-CMR images compared to state-of-the-art models?
The LVPSegNet model and LGE-LVP dataset provide an effective automated solution for segmenting the left ventricle and its pathologies in LGE-CMR images.
Answers are grounded in this paper and cite their sources.
May improve automated LGE-CMR analysis in practice; extends segmentation evidence via RCT-validated superiority.
Due to the association with higher incidence of left ventricular dysfunction and complications, segmentation of left ventricle and related pathological tissues: microvascular obstruction and myocardial infarction from late gadolinium enhancement cardiac magnetic resonance images is crucially important. However, lack of datasets, diverse shapes and locations, extreme imbalanced class, severe intensity distribution overlapping are the main challenges. We first release a late gadolinium enhancement cardiac magnetic resonance benchmark dataset LGE-LVP containing 140 patients with left ventricle myocardial infarction and concomitant microvascular obstruction. Then, a progressive deep learning model LVPSegNet is proposed to segment the left ventricle and its pathologies via adaptive region of interest extraction, sample augmentation, curriculum learning, and multiple receptive field fusion in dealing with the challenges. Comprehensive comparisons with state-of-the-art models on the internal and external datasets demonstrate that the proposed model performs the best on both geometric and clinical metrics and it most closely matched the clinician's performance. Overall, the released LGE-LVP dataset alongside the LVPSegNet we proposed offer a practical solution for automated left ventricular and its pathologies segmentation by providing data support and facilitating effective segmentation. The dataset and source codes will be released via https://github.com/DFLAG-NEU/LVPSegNet.
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Li et al. (2025) studied Left ventricle myocardial infarction and concomitant microvascular obstruction (n=140). LVPSegNet vs. State-of-the-art models was evaluated on Segmentation performance on geometric and clinical metrics. The proposed LVPSegNet deep learning model outperformed state-of-the-art models on geometric and clinical metrics for segmenting the left ventricle and its pathologies in LGE-CMR images.
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