Multi-resolution image segmentation based on a cascaded u-adensenet for the liver and tumors

Yan Zhu, Aihong Yu, Huan Rong, Dongqing Wang, Yuqing Song, Zhe Liu, Victor S. Sheng

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number1044
JournalJournal of Personalized Medicine
Volume11
Issue number10
DOIs
StatePublished - Oct 2021

Keywords

  • CT images
  • Cascaded
  • Channel attention
  • Convolutional neural network
  • Liver segmentation

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