Multi-Resolution Image Segmentation Based on a Cascaded U-ADenseNet for the Liver and Tumors

Abstract

The liver is an irreplaceable organ in the human body, maintaining life activities and metabolism. Malignant tumors of the liver have a high mortality rate at present. Computer-aided segmentation of the liver and tumors has significant effects on clinical diagnosis and treatment. There are still many challenges in the segmentation of the liver and liver tumors simultaneously, such as, on the one hand, that convolutional kernels with fixed geometric structures do not match complex, irregularly shaped targets; on the other, pooling during convolution results in a loss of spatial contextual information of images. In this work, we designed a cascaded U-ADenseNet with coarse-to-fine processing for addressing the above issues of fully automatic segmentation. This work contributes multi-resolution input images and multi-layered channel attention combined with atrous spatial pyramid pooling densely connected in the fine segmentation. The proposed model was evaluated by a public dataset of the Liver Tumor Segmentation Challenge (LiTS). Our approach attained competitive liver and tumor segmentation scores that exceeded other methods across a wide range of metrics.

Description

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords

CT Images, Convolutional Neural Network, Channel Attention, Cascaded, Liver Segmentation

Citation

Zhu Y, Yu A, Rong H, Wang D, Song Y, Liu Z, Sheng VS. Multi-Resolution Image Segmentation Based on a Cascaded U-ADenseNet for the Liver and Tumors. Journal of Personalized Medicine. 2021; 11(10):1044. https://doi.org/10.3390/jpm11101044

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