机构：[a]Department of Radiology and BRIC,University of North Carolina at Chapel Hill,Chapel Hill,NC 27599,USA[b]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China[c]Department of Information Science and Engineering,Chongqing Jiaotong University,Chongqing 400074,China[d]Department of Radiology,Xuanwu Hospital,Capital Medical University,Beijing 100053,China放射科首都医科大学宣武医院[e]Digital Medical Research Center,School of Basic Medical Science,Fudan University,Shanghai 200032,China[f]Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention,Shanghai 200032,China[g]Department of Brainand Cognitive Engineering,Korea University,Seou l02841,Republic of Korea
Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer (label-specific dictionaries) consists of groups of representative atlas patches and the subsequent layers (residual dictionaries) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared to other counterpart label fusion methods. (C) 2016 Elsevier Ltd. All rights reserved.
基金：
National Institutes of Health (NIH) grants HD081467, EB006733, EB008374, EB009634, MH100217, AG041721, AG049371, AG049089, AG042599, CA140413
第一作者机构：[a]Department of Radiology and BRIC,University of North Carolina at Chapel Hill,Chapel Hill,NC 27599,USA[b]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
通讯作者：
通讯机构：[a]Department of Radiology and BRIC,University of North Carolina at Chapel Hill,Chapel Hill,NC 27599,USA[g]Department of Brainand Cognitive Engineering,Korea University,Seou l02841,Republic of Korea
推荐引用方式(GB/T 7714)：
Chen Zu,ZhengxiaWang,DaoqiangZhang,et al.Robust multi-atlas label propagation by deep sparse representation[J].PATTERN RECOGNITION.2017,63:511-517.doi:10.1016/j.patcog.2016.09.028.
APA：
Chen Zu,ZhengxiaWang,DaoqiangZhang,PeipengLiang,YonghongShi...&GuorongWu.(2017).Robust multi-atlas label propagation by deep sparse representation.PATTERN RECOGNITION,63,
MLA：
Chen Zu,et al."Robust multi-atlas label propagation by deep sparse representation".PATTERN RECOGNITION 63.(2017):511-517
计算机科学