Segmentation of Liver Lesions with Reduced Complexity Deep Models

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 a...

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Bibliographic Details
Published in:arXiv.org 2018-05
Main Authors: Pandey, Ram Krishna, Vasan, Aswin, Ramakrishnan, A G
Format: Article
Language:English
Subjects:
Computed tomography
Convolution
Image segmentation
Interpolation
Lesions
Liver
Mathematical models
Parameters
Pixels
Online Access:Get full text
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Description
Summary: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.
ISSN:2331-8422