Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating spatial correlation for WHS. The network performs slice-by-slice segmentation on three-dimensional cardiac CT images in a 2D encoder–decoder manner. In the network, a convolutional long short-term memory skip connection module is designed to perform spatial correlation feature extraction on the feature maps at different resolutions extracted by the sub-modules of the pre-trained ResNet-based encoder. Moreover, a decoder based on the multi-residual module is designed to analyze the extracted features from the perspectives of multi-scale and channel attention, thereby accurately delineating the various substructures of the heart. The proposed method is verified on a dataset of the multi-modality WHS challenge, an in-house WHS dataset, and a dataset of the abdominal organ segmentation challenge. The dice, Jaccard, average symmetric surface distance, Hausdorff distance, inference time, and maximum GPU memory of the WHS are 0.914, 0.843, 1.066 mm, 15.778 mm, 9.535 s, and 1905 MB, respectively. The proposed network has high accuracy, fast inference speed, minimal GPU memory consumption, strong robustness, and good generalization. It can be deployed to clinical practical applications for WHS and can be effectively extended and applied to other multi-organ segmentation fields. The source code is publicly available at .

中文翻译：

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集成空间相关性的多残差 2D 网络用于全心脏分割


全心脏分割（WHS）对于心脏解剖、心脏功能建模和分析具有重要的临床价值。本研究旨在解决心脏 CT 图像的 WHS 准确性，以及实际临床应用所需的快速推理速度和低图形处理单元 (GPU) 内存消耗。因此，我们提出了一种集成 WHS 空间相关性的多残差二维 (2D) 网络。该网络以 2D 编码器-解码器的方式对三维心脏 CT 图像进行逐层分割。在网络中，设计了一个卷积长短期记忆跳跃连接模块，对预训练的基于ResNet的编码器的子模块提取的不同分辨率的特征图进行空间相关特征提取。此外，设计了基于多残差模块的解码器，从多尺度和通道注意力的角度分析提取的特征，从而准确地描绘心脏的各个子结构。所提出的方法在多模态 WHS 挑战数据集、内部 WHS 数据集和腹部器官分割挑战数据集上进行了验证。 WHS 的骰子、Jaccard、平均对称表面距离、Hausdorff 距离、推理时间和最大 GPU 内存分别为 0.914、0.843、1.066 mm、15.778 mm、9.535 s 和 1905 MB。所提出的网络具有精度高、推理速度快、GPU内存消耗最小、鲁棒性强、泛化性好的特点。它可以部署到WHS的临床实际应用中，并可以有效扩展和应用到其他多器官分割领域。源代码可在 公开获取。