A Multi-scale Attention Network for Kidney Tumor Segmentation on CT Scans

Authors
Sun, Liyan
Zeng, Weihong
Ding, Xinghao
Huang, Yue

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Automatic kidney segmentation is a promising tool for developing advanced surgical planning techniques. However, due to the high morphological heterogeneity within the kidney CT data, the automatic segmentation of kidney and tumor is a difficult problem. Although the state-of-the-art 3D U-Net provides accurate segmentations of medical images, the multi-scale information is underutilized. We propose a multi-scale attention network (MSAN) for automatic kidney tumor segmentation. A multi-scale attention layer is developed to combine the local and global contextual information. Furthermore, a ensemble strategy based on voting mechanism boosts the model performance. We achieve the averaged Dice coefficients 94.83% on kidney and 64.89% on tumor in the validation datasets.

Identifiers
doi: 10.24926/548719.055