We propose a computationally efficient architecture that learns to segment lesions from CT images of the liver. The proposed architecture uses bilinear interpolation with sub-pixel convolution at the last layer to upscale the course feature in bottle neck architecture. Since bilinear interpolation and sub-pixel convolution do not have any learnable parameter, our overall model is faster and occupies less memory footprint than the traditional U-net. We evaluate our proposed architecture on the highly competitive dataset of 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method achieves competitive results while reducing the number of learnable parameters roughly by a factor of 13.8 compared to the original UNet model.
@article{arxiv.1805.09233,
title = {Segmentation of Liver Lesions with Reduced Complexity Deep Models},
author = {Ram Krishna Pandey and Aswin Vasan and A G Ramakrishnan},
journal= {arXiv preprint arXiv:1805.09233},
year = {2018}
}