Accurate reorientation and segmentation of the left ventricular (LV) is
essential for the quantitative analysis of myocardial perfusion imaging (MPI),
in which one critical step is to reorient the reconstructed transaxial nuclear
cardiac images into standard short-axis slices for subsequent image processing.
Small-scale LV myocardium (LV-MY) region detection and the diverse cardiac
structures of individual patients pose challenges to LV segmentation operation.
To mitigate these issues, we propose an end-to-end model, named as multi-scale
spatial transformer UNet (MS-ST-UNet), that involves the multi-scale spatial
transformer network (MSSTN) and multi-scale UNet (MSUNet) modules to perform
simultaneous reorientation and segmentation of LV region from nuclear cardiac
images. The proposed method is trained and tested using two different nuclear
cardiac image modalities: 13N-ammonia PET and 99mTc-sestamibi SPECT. We use a
multi-scale strategy to generate and extract image features with different
scales. Our experimental results demonstrate that the proposed method
significantly improves the reorientation and segmentation performance. This
joint learning framework promotes mutual enhancement between reorientation and
segmentation tasks, leading to cutting edge performance and an efficient image
processing workflow. The proposed end-to-end deep network has the potential to
reduce the burden of manual delineation for cardiac images, thereby providing
multimodal quantitative analysis assistance for physicists.

我们提出了一种名为多尺度空间变换 UNet（MS-ST-UNet）的端到端模型，用于从核心心脏图像中同时重新定位和分割左室（LV）区域。该方法采用多尺度策略生成和提取具有不同尺度的图像特征，并通过联合学习促进重新定位和分割任务之间的互相增强。该方法显著改进了重新定位和分割性能，能够为物理学家提供多模态定量分析辅助，减少手动描绘心脏图像的负担，并提供高效的图像处理工作流程。

用于自动 3D 核心心脏图像重新定向和分割的多尺度空间变换器 U-Net

A Multi-Scale Spatial Transformer U-Net for Simultaneously Automatic  Reorientation and Segmentation of 3D Nuclear Cardiac Images

Quantitative assessment of left ventricle (LV) function from cine MRI has
significant diagnostic and prognostic value for cardiovascular disease
patients. The temporal movement of LV provides essential information on the
contracting/relaxing pattern of heart, which is keenly evaluated by clinical
experts in clinical practice. Inspired by the expert way of viewing Cine MRI,
we propose a new CNN module that is able to incorporate the temporal
information into LV segmentation from cine MRI. In the proposed CNN, the
optical flow (OF) between neighboring frames is integrated and aggregated at
feature level, such that temporal coherence in cardiac motion can be taken into
account during segmentation. The proposed module is integrated into the U-net
architecture without need of additional training. Furthermore, dilated
convolution is introduced to improve the spatial accuracy of segmentation.
Trained and tested on the Cardiac Atlas database, the proposed network resulted
in a Dice index of 95% and an average perpendicular distance of 0.9 pixels for
the middle LV contour, significantly outperforming the original U-net that
processes each frame individually. Notably, the proposed method improved the
temporal coherence of LV segmentation results, especially at the LV apex and
base where the cardiac motion is difficult to follow.

提出了一种基于卷积神经网络的新的左心室分割算法，能够充分利用心脏运动的时间一致性，从而显著提高左心室分割的精度。该算法在 Cardiac Atlas 数据库上进行了训练和测试，结果表明，在 Dice 系数和平均垂直距离等指标上都明显优于原有的 U-net 模型。