标题:A multi-view deep convolutional neural networks for lung nodule segmentation
作者:Wang, Shuo ;Zhou, Mu ;Gevaert, Olivier ;Tang, Zhenchao ;Dong, Di ;Liu, Zhenyu ;Tian, Jie
作者机构:[Wang, Shuo ;Dong, Di ;Liu, Zhenyu ;Tian, Jie ] CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 更多
会议名称:39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
会议日期:July 11, 2017 - July 15, 2017
来源:Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
出版年:2017
页码:1752-1755
DOI:10.1109/EMBC.2017.8037182
摘要:We present a multi-view convolutional neural networks (MV-CNN) for lung nodule segmentation. The MV-CNN specialized in capturing a diverse set of nodule-sensitive features from axial, coronal and sagittal views in CT images simultaneously. The proposed network architecture consists of three CNN branches, where each branch includes seven stacked layers and takes multi-scale nodule patches as input. The three CNN branches are then integrated with a fully connected layer to predict whether the patch center voxel belongs to the nodule. The proposed method has been evaluated on 893 nodules from the public LIDC-IDRI dataset, where ground-truth annotations and CT imaging data were provided. We showed that MV-CNN demonstrated encouraging performance for segmenting various type of nodules including juxta-pleural, cavitary, and non-solid nodules, achieving an average dice similarity coefficient (DSC) of 77.67% and average surface distance (ASD) of 0.24, outperforming conventional image segmentation approaches. © 2017 IEEE.
收录类别:EI
资源类型:会议论文
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