Ye Dong Hye, Desjardins Benoit, Ferrari Victor, Metaxas Dimitris, Pohl Kilian M
Department of Electrical and Computer Engineering, Purdue University.
Department of Radiology, University of Pennsylvania.
Proc IEEE Int Symp Biomed Imaging. 2014 Apr-May;2014:217-221. doi: 10.1109/ISBI.2014.6867848. Epub 2014 Jul 31.
We propose a fully-automatic morphometric encoding targeted towards differentiating diseased from healthy cardiac MRI. Existing encodings rely on accurate segmentations of each scan. Segmentation generally includes labour-intensive editing and increases the risk associated with intra- and inter-rater variability. Our morphometric framework only requires the segmentation of a template scan. This template is non-rigidly registered to the other scans. We then confine the resulting deformation maps to the regions outlined by the segmentations. We learn a manifold for each region and identify the most informative coordinates with respect to distinguishing diseased from healthy scans. Compared with volumetric measurements and a deformation-based score, this encoding is much more accurate in capturing morphometric patterns distinguishing healthy subjects from those with Tetralogy of Fallot, diastolic dysfunction, and hypertrophic cardiomyopathy.
我们提出了一种全自动形态计量编码方法，旨在区分心脏疾病的MRI图像与健康的MRI图像。现有的编码方法依赖于对每次扫描进行精确分割。分割通常包括劳动强度大的编辑工作，并且会增加评分者内和评分者间变异性相关的风险。我们的形态计量框架仅需要对模板扫描进行分割。该模板与其他扫描进行非刚性配准。然后，我们将得到的变形图限制在分割轮廓所勾勒的区域内。我们为每个区域学习一个流形，并识别出区分疾病扫描与健康扫描最具信息性的坐标。与体积测量和基于变形的评分相比，这种编码在捕捉区分健康受试者与法洛四联症、舒张功能障碍和肥厚型心肌病患者的形态计量模式方面要准确得多。
