Anatomically-guided deep learning for left ventricle geometry generation with uncertainty quantification based on short-axis MR images
Anatomically-guided deep learning for left ventricle geometry generation with uncertainty quantification based on short-axis MR images
复制标题
基于短轴 MR 图像的解剖学引导深度学习，通过不确定性量化生成左心室几何形状
DOI：
10.1016/j.engappai.2023.106012
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发表时间：
2023
影响因子：
8
通讯作者：
Viana, Felipe A.C.
中科院分区：
文献类型：
--
作者：
Zuben, Andre Von;Perotti, Luigi E.;Viana, Felipe A.C.
深度学习的最新进展大大提高了从医学图像生成分析模型的能力。特别是，极大的注意力集中在从心脏磁共振成像（cMRI）快速生成左心室模型，以改善数百万患者的诊断和预后。然而，即使是最先进的框架也存在挑战，例如心脏组织的不连续性和沿着心肌壁的过度锯齿状沿着。这些几何特征通常在解剖学上是不正确的，并且一旦在计算分析中采用几何模型就可能导致不切实际的结果。在这项工作中，我们提出了一种解剖学引导的深度学习模型来克服这些限制，同时保留了最先进框架的优点，如计算效率，鲁棒性和泛化能力。我们的新型解剖学引导神经网络是由UNet和B样条头形成的，B样条头在训练过程中充当正则化层。B样条头将预测聚合到单个连接区域中，去除任何不需要的组织岛，并产生平滑的连续轮廓。此外，B样条头的引入有助于实现左心室内外壁的鲁棒不确定性量化。我们的研究结果表明，所提出的模型生成解剖学上一致的几何形状，同时实现了与地面实况图像相媲美的最先进的框架，同时提高了几何不确定性量化相比，经典的UNet模型。这里提供的示例以及源代码都是在GitHub存储库https://github.com/CBL-UCF/unet_ag下开源的。
Recent advances in deep learning have greatly improved the ability to generate analysis models from medical images. In particular, great attention is focused on quickly generating models of the left ventricle from cardiac magnetic resonance imaging (cMRI) to improve the diagnosis and prognosis of millions of patients. However, even state-of-the art frameworks present challenges, such as discontinuities of the cardiac tissue and excessive jaggedness along the myocardial walls. These geometrical features are often anatomically incorrect and may lead to unrealistic results once the geometrical models are employed in computational analyses. In this work, we propose an anatomically-guided deep learning model to overcome these limitations while preserving the advantages of state-of-the-art frameworks, such as computational efficiency, robustness, and generalization capabilities. Our novel anatomically-guided neural networks are formed by a UNet followed by a B-spline head, which acts as a regularization layer during training. The B-spline head aggregates the prediction into a single connected region, removes any undesired tissue islands, and produces a smooth continuous contour. In addition, the introduction of the B-spline head contributes to achieve a robust uncertainty quantification of the left ventricle inner and outer walls. Our results show that the proposed model generates anatomically consistent geometries while achieving an agreement with the ground truth images comparable to state-of-the-art frameworks and simultaneously improving the geometry uncertainty quantification in comparison to classic UNet models. The examples presented here, as well as source codes, are all open-source under the GitHub repository https://github.com/CBL-UCF/unet_ag.