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arXiv · 1805.09233

Segmentation of Liver Lesions with Reduced Complexity Deep Models

Ram Krishna Pandey·Aswin Vasan·A G Ramakrishnan
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Abstract

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.

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Ram Krishna Pandey, Aswin Vasan, A G Ramakrishnan. 2018-05-23. Segmentation of Liver Lesions with Reduced Complexity Deep Models. https://arxiv.org/abs/1805.09233

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