A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images

摘要：

While there has been substantial progress in segmenting natural images, state-of-the-art methods that perform well in such tasks unfortunately tend to underperform when confronted with the different challenges posed by electron microscope (EM) data. For example, in EM imagery of neural tissue, numerous cells and subcellular structures appear within a single image, they exhibit irregular shapes that cannot be easily modeled by standard techniques, and confusing textures clutter the background. We propose a fully automated approach that handles these challenges by using sophisticated cues that capture global shape and texture information, and by learning the specific appearance of object boundaries. We demonstrate that our approach significantly outperforms state-of-the-art techniques and closely matches the performance of human annotators.

作者: Aurelien Lucchi Kevin Smith Radhakrishna Achanta Vincent Lepetit Pascal Fua

作者单位: Computer Vision Lab, Ecole Polytechnique Federale de Lausanne, Switzerland Computer Vision Lab, Ecole Polytechnique F′ed′erale de Lausanne, Switzerland

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 463-471

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）