Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2RH London, UK.
IEEE Trans Med Imaging. 2013 Jul;32(7):1302-15. doi: 10.1109/TMI.2013.2256922. Epub 2013 Apr 5.
The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It typically involves measurement of the left ventricular (LV) mass and LV cavity volume. Manual delineation of the myocardial contours is time-consuming and dependent on the subjective experience of the expert observer. In this paper, a multi-atlas method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves image registration accuracy by utilizing label information, which leads to improvement of segmentation accuracy. The proposed method was evaluated on a cardiac MR image set of 28 subjects. The average Dice overlap metric of our segmentation is 0.92 for the LV cavity, 0.89 for the right ventricular cavity and 0.82 for the myocardium. The results show that the proposed method is able to provide accurate information for clinical diagnosis.
心室功能评估对于心血管疾病的诊断很重要。它通常涉及左心室（LV）质量和 LV 腔体积的测量。心肌轮廓的手动描绘既费时又依赖于专家观察者的主观经验。在本文中，提出了一种用于心脏磁共振（MR）图像分割的多图谱方法。所提出的方法在两个方面具有创新性。首先，它在贝叶斯框架中构建了基于补丁的标签融合模型。其次，它通过利用标签信息来提高图像配准的准确性，从而提高分割的准确性。该方法在 28 名受试者的心脏磁共振图像集上进行了评估。LV 腔的平均 Dice 重叠度量为 0.92，右心室腔为 0.89，心肌为 0.82。结果表明，所提出的方法能够为临床诊断提供准确的信息。
