Authors
Tao, Qingyi
Wu, Zhonghua
Cheong, Isaac
Yang, Jingyi
Ge, Zongyuan
Lin, Guosheng
Cai, Jianfei
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Issue Date
2019
Publisher
University of Minnesota Libraries Publishing
Type
Article
Abstract
To study the kidney diseases and kidney tumor from Computed Tomography(CT) imaging data, it is helpful to segment the region of interest through computer aided auto-segmentation tool. In the KiTs 2019 challenge [1], we are provided 3D volumetric CT data to train a model for kidney and kidney tumor segmentation. We introduce an improved deep 3D Unet by enriching the feature representation in CT images using an attention module. We achieve 1.5% improvement in the segmentation accuracy when evaluated on the validation set.
Identifiers
doi: 10.24926/548719.016