Abdomen tissues segmentation from computed tomography images using deep learning and level set methods

Zhaoxuan Gong; Jing Song; Wei Guo; Ronghui Ju; Dazhe Zhao; Wenjun Tan; Wei Zhou; Guodong Zhang; Zhaoxuan Gong; Jing Song; Wei Guo; Ronghui Ju; Dazhe Zhao; Wenjun Tan; Wei Zhou; Guodong Zhang

doi:10.3934/mbe.2022655

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 14074-14085. doi: 10.3934/mbe.2022655

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Research article Special Issues

Abdomen tissues segmentation from computed tomography images using deep learning and level set methods

1.
Department of Computer Science and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
2.
Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, China
3.
Liaoning provincial people's hospital, Shenyang 110067, China

Academic Editor: Simon James Fong

Received: 27 July 2022 Revised: 06 September 2022 Accepted: 08 September 2022 Published: 23 September 2022

Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surrounding organs. In this paper, an attention-based deep learning method for automated abdomen tissues segmentation is proposed. In our method, image cropping is first applied to the original images. U-net model with attention mechanism is then constructed to obtain the initial abdomen tissues. Finally, level set evolution which consists of three energy terms is used for optimize the initial abdomen segmentation. The proposed model is evaluated across 470 subsets. For liver segmentation, the mean dice are 96.2 and 95.1% for the FLARE21 datasets and the LiTS datasets, respectively. For kidney segmentation, the mean dice are 96.6 and 95.7% for the FLARE21 datasets and the LiTS datasets, respectively. Experimental evaluation exhibits that the proposed method can obtain better segmentation results than other methods.
- abdomen segmentation,
- image cropping,
- deep learning,
- CT image,
- level set evolution
Citation: Zhaoxuan Gong, Jing Song, Wei Guo, Ronghui Ju, Dazhe Zhao, Wenjun Tan, Wei Zhou, Guodong Zhang. Abdomen tissues segmentation from computed tomography images using deep learning and level set methods[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 14074-14085. doi: 10.3934/mbe.2022655

Related Papers:

Abstract

Accurate abdomen tissues segmentation is one of the crucial tasks in radiation therapy planning of related diseases. However, abdomen tissues segmentation (liver, kidney) is difficult because the low contrast between abdomen tissues and their surrounding organs. In this paper, an attention-based deep learning method for automated abdomen tissues segmentation is proposed. In our method, image cropping is first applied to the original images. U-net model with attention mechanism is then constructed to obtain the initial abdomen tissues. Finally, level set evolution which consists of three energy terms is used for optimize the initial abdomen segmentation. The proposed model is evaluated across 470 subsets. For liver segmentation, the mean dice are 96.2 and 95.1% for the FLARE21 datasets and the LiTS datasets, respectively. For kidney segmentation, the mean dice are 96.6 and 95.7% for the FLARE21 datasets and the LiTS datasets, respectively. Experimental evaluation exhibits that the proposed method can obtain better segmentation results than other methods.

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