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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Simulated 3D Ultrasound LV Cardiac Images for Active Shape Model Training
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Simulated 3D Ultrasound LV Cardiac Images for Active Shape Model Training

机译：用于主动形状模型训练的模拟3D超声LV心脏图像

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摘要

In this paper a study of 3D ultrasound cardiac segmentation using Active Shape Models (ASM) is presented. The proposed approach is based on a combination of a point distribution model constructed from a multitude of high resolution MRI scans and the appearance model obtained from simulated 3D ultrasound images. Usually the appearance model is learnt from a set of landmarked images. The significant level of noise, the low resolution of 3D ultrasound images (3D US) and the frequent failure to capture the complete wall of the left ventricle (LV) makes automatic or manual landmarking difficult. One possible solution is to use artificially simulated 3D US images since the generated images will match exactly the shape in question. In this way, by varying simulation parameters and generating corresponding images, it is possible to obtain a training set where the image matches the shape exactly. In this work the simulation of ultrasound images is performed by a convolutional approach. The evaluation of segmentation accuracy is performed on both simulated and in vivo images. The results obtained on 567 simulated images had an average error of 1.9 mm (1.73 ± 0.05 mm for epicardium and 2 ± 0.07 mm for endocardium, with 95% confidence) with voxel size being 1.1 × 1.1 × 0.7 mm. The error on 20 in vivo data was 3.5 mm (3.44 ± 0.4 mm for epicardium and 3.73 ± 0.4 mm for endocardium). In most images the model was able to approximate the borders of myocardium even when the latter was indistinguishable from the surrounding tissues.
机译：在本文中，提出了使用活动形状模型（ASM）进行3D超声心脏分割的研究。所提出的方法基于由多个高分辨率MRI扫描构建的点分布模型与从模拟3D超声图像获得的外观模型的组合。通常，外观模型是从一组地标图像中学习的。较大的噪声水平，3D超声图像的分辨率较低（3D US）以及无法捕获完整的左心室壁（LV）的频繁失败，使自动或手动地标变得困难。一种可能的解决方案是使用人工模拟的3D US图像，因为生成的图像将完全匹配所讨论的形状。这样，通过改变模拟参数并生成相应的图像，可以获得图像与形状精确匹配的训练集。在这项工作中，超声图像的模拟是通过卷积方法执行的。分割准确性的评估是在模拟图像和体内图像上进行的。在567张模拟图像上获得的结果的平均误差为1.9 mm（心外膜为1.73±0.05 mm，内膜为2±0.07 mm，置信度为95％），体素大小为1.1×1.1×0.7 mm。 20个体内数据的误差为3.5毫米（心外膜为3.44±0.4毫米，心内膜为3.73±0.4毫米）。在大多数图像中，即使心肌与周围组织无法区分，该模型也能够近似心肌的边界。

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