通过分类器引导的两阶段网络与全切片信息融合Transformer增强心脏MRI分割
Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer
AI总结：
本研究提出一种分类器引导的两阶段网络结合全切片融合Transformer，以提升心脏MRI分割准确性，尤其在基底和心尖切片，并在Dice分数上优于现有模型。
摘要：
心脏磁共振成像（CMR）是评估心脏功能的金标准。在CMR图像中分割左心室（LV）、右心室（RV）和左心室心肌（MYO）至关重要但耗时。基于深度学习的分割方法已成为自动化这一过程的有效工具。然而，由于心脏形状不规则且多变，特别是在基底和心尖切片中，CMR图像带来了额外挑战。在本研究中，我们提出了一种带有全切片融合Transformer的分类器引导的两阶段网络，以提高CMR分割的准确性，尤其是在基底和心尖切片中。我们的方法在广泛的临床数据集上进行了评估，并在Dice分数方面表现出优于以往基于CNN和基于Transformer的模型的性能。此外，我们的方法产生的分割形状在视觉上类似于人工标注，避免了其他模型分割中常见的空洞或碎片问题。
英文摘要：
Cardiac Magnetic Resonance imaging (CMR) is the gold standard for assessing cardiac function. Segmenting the left ventricle (LV), right ventricle (RV), and LV myocardium (MYO) in CMR images is crucial but time-consuming. Deep learning-based segmentation methods have emerged as effective tools for automating this process. However, CMR images present additional challenges due to irregular and varying heart shapes, particularly in basal and apical slices. In this study, we propose a classifier-guided two-stage network with an all-slice fusion transformer to enhance CMR segmentation accuracy, particularly in basal and apical slices. Our method was evaluated on extensive clinical datasets and demonstrated better performance in terms of Dice score compared to previous CNN-based and transformer-based models. Moreover, our method produces visually appealing segmentation shapes resembling human annotations and avoids common issues like holes or fragments in other models' segmentations.
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