Hanaoka Shouhei, Masutani Yoshitaka, Nemoto Mitsutaka, Nomura Yukihiro, Yoshikawa Takeharu, Hayashi Naoto, Ohtomo Kuni
Department of Radiology, Graduate School of Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):106-13. doi: 10.1007/978-3-642-33418-4_14.
A method for categorizing landmark-local appearances extracted from computed tomography (CT) datasets is presented. Anatomical landmarks in the human body inevitably have inter-individual variations that cause difficulty in automatic landmark detection processes. The goal of this study is to categorize subjects (i.e., training datasets) according to local shape variations of such a landmark so that each subgroup has less shape variation and thus the machine learning of each landmark detector is much easier. The similarity between each subject pair is measured based on the non-rigid registration result between them. These similarities are used by the spectral clustering process. After the clustering, all training datasets in each cluster, as well as synthesized intermediate images calculated from all subject-pairs in the cluster, are used to train the corresponding subgroup detector. All of these trained detectors compose a detector ensemble to detect the target landmark. Evaluation with clinical CT datasets showed great improvement in the detection performance.
提出了一种对从计算机断层扫描（CT）数据集中提取的地标局部外观进行分类的方法。人体中的解剖地标不可避免地存在个体间差异，这给自动地标检测过程带来了困难。本研究的目标是根据此类地标的局部形状变化对受试者（即训练数据集）进行分类，以便每个亚组的形状变化较小，从而使每个地标检测器的机器学习变得更加容易。基于每对受试者之间的非刚性配准结果来测量它们之间的相似度。这些相似度用于谱聚类过程。聚类后，每个聚类中的所有训练数据集以及从聚类中的所有受试者对计算出的合成中间图像，都用于训练相应的亚组检测器。所有这些经过训练的检测器组成一个检测器集成来检测目标地标。使用临床CT数据集进行的评估显示检测性能有了很大提高。
