Oguz Ipek, Kashyap Satyananda, Wang Hongzhi, Yushkevich Paul, Sonka Milan
Department of Radiology, University of Pennsylvania, Philadelphia, USA.
Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:538-546. doi: 10.1007/978-3-319-46723-8_62. Epub 2016 Oct 2.
Multi-atlas label fusion methods have gained popularity in a variety of segmentation tasks given their attractive performance. Graph-based segmentation methods are widely used given their global optimality guarantee. We propose a novel approach, GOLF, that combines the strengths of these two approaches. GOLF incorporates shape priors to the label-fusion problem and provides a globally optimal solution even for the multi-label scenario, while also leveraging the highly accurate posterior maps from a multi-atlas label fusion approach. We demonstrate GOLF for the joint segmentation of the left and right pairs of caudate, putamen, globus pallidus and nucleus accumbens. Compared to the FreeSurfer and FIRST approaches, GOLF is significantly more accurate on all reported indices for all 8 structures. We also present comparisons to a multi-atlas approach, which reveals further insights on the contributions of the different components of the proposed framework.
鉴于其出色的性能，多图谱标签融合方法在各种分割任务中受到了广泛欢迎。基于图的分割方法因其全局最优性保证而被广泛使用。我们提出了一种新颖的方法——GOLF，它结合了这两种方法的优势。GOLF将形状先验纳入标签融合问题，即使在多标签场景下也能提供全局最优解，同时还利用了多图谱标签融合方法生成的高精度后验图谱。我们展示了GOLF用于尾状核、壳核、苍白球和伏隔核左右对的联合分割。与FreeSurfer和FIRST方法相比，GOLF在所有8个结构的所有报告指标上都显著更准确。我们还与一种多图谱方法进行了比较，这揭示了对所提出框架不同组件贡献的进一步见解。
