通过动脉壁掩膜改善CCTA-CPR扫描中钙化与非钙化斑块的分割
Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall
AI总结：
本文提出通过动脉壁掩膜减少体素模糊性，利用深度学习血管检测器自动生成内外壁轮廓，从而显著提升CCTA-CPR图像中钙化与非钙化斑块的分割性能。
摘要：
冠状动脉中斑块的存在是患者生命的主要风险。特别是非钙化斑块构成了巨大挑战，因为它们比钙化斑块更难检测且更易破裂。尽管当前的深度学习技术能够对真实生活图像进行精确分割，但在医学图像中的表现仍然较低。这主要是由模糊性以及无关部分落在相同数值范围内的模糊体素强度造成的。在本文中，我们提出了一种在冠状动脉CCTA-CPR扫描中分割钙化和非钙化斑块的新方法。对输入切片进行掩膜处理，使得仅考虑血管壁内的体素进行分割，从而减少模糊性。该掩膜可以通过基于深度学习的血管检测器自动生成，该检测器不仅提供动脉外壁的轮廓，还提供内壁的轮廓。为了进行评估，我们使用了一个数据集，其中每个体素都被仔细标注为五个类别之一：背景、管腔、动脉壁、钙化斑块或非钙化斑块。我们还通过应用不同类型的掩膜进行了详尽的评估，以验证血管掩膜在斑块分割中的潜力。我们的方法在定量和定性评估中均显著提升了分割性能，即使对于具有挑战性的非钙化斑块也能获得准确的斑块形状。此外，当使用高精度掩膜时，诸如狭窄等困难病例也变得可分割。我们相信我们的发现能够引领未来高性能斑块分割的研究。
英文摘要：
The presence of plaques in the coronary arteries is a major risk to the patients' life. In particular, non-calcified plaques pose a great challenge, as they are harder to detect and more likely to rupture than calcified plaques. While current deep learning techniques allow precise segmentation of real-life images, the performance in medical images is still low. This is caused mostly by blurriness and ambiguous voxel intensities of unrelated parts that fall on the same value range. In this paper, we propose a novel methodology for segmenting calcified and non-calcified plaques in CCTA-CPR scans of coronary arteries. The input slices are masked so only the voxels within the wall vessel are considered for segmentation, thus, reducing ambiguity. This mask can be automatically generated via a deep learning-based vessel detector, that provides not only the contour of the outer artery wall, but also the inner contour. For evaluation, we utilized a dataset in which each voxel is carefully annotated as one of five classes: background, lumen, artery wall, calcified plaque, or non-calcified plaque. We also provide an exhaustive evaluation by applying different types of masks, in order to validate the potential of vessel masking for plaque segmentation. Our methodology results in a prominent boost in segmentation performance, in both quantitative and qualitative evaluation, achieving accurate plaque shapes even for the challenging non-calcified plaques. Furthermore, when using highly accurate masks, difficult cases such as stenosis become segmentable. We believe our findings can lead the future research for high-performance plaque segmentation.
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