Convolutional Neural Network for Kidney and kidney Tumor segmentation

Authors
Kumar Anand, Vikas
Aurangabadkar, Pranav
Khened, Mahendra
Krishnamurthi, Ganapathy

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
In this work, we have attempted to develop algorithms for automatic segmentation of kidney and kidney tumous from CT images. We have exploited encoder decoder architecture of fully convolutional neural network.Pre-processesing steps involves slice extraction, data standardization and Hounsfield unit windowing. The proposed network has been trained on CT images of kidney and kidney tumors with their ground truth. Weighted combination of focal loss and dice loss has been minimized using Adam as optimizer. Dice coefficient of 94.68% and 94.51% has been achieved for kidney and kidney tumor segmentation respectively.

Identifiers
doi: 10.24926/548719.039