H-DenseUNet for Kidney and Tumor Segmentation from CT Scans

Authors
Li, Xiaomeng
Liu, Lihao
Heng, Pheng-Ann

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
Automatic kidney tumor segmentation from CT scans is an essential step for computer-aided diagnosis of cancer. In this paper, we present an improved H-DenseUNet for kidney and tumor segmentation. Specifically, we first train the DenseUNet and then fine tune the network with the 3D counterpart. To further increase the performance, we employ both cross-entropy and dice loss. We evaluate our method on the 2019 MICCAI kidney and tumor segmentation challenge. We split the training dataset of the challenge to 200 training set and 10 validation set. On the validation set, our method achieves 97.0% (Dice) for kidney segmentation and 67.2% (Dice) for tumor segmentation. This model is submitted to the challenge for final performance evaluation on the test dataset.

Identifiers
doi: 10.24926/548719.027