A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab

  • Wei Tang
  • Dongsheng ZouEmail author
  • Su Yang
  • Jing Shi
  • Jingpei Dan
  • Guowu Song
S.I. : Brain inspired Computing &Machine Learning Applied Research-BISMLARE


Proper liver segmentation is a key step in many clinical applications, including computer-assisted diagnosis, radiation therapy and volume measurement. However, liver segmentation is still challenging due to fuzzy boundary, complex liver anatomy, present of pathologies, and diversified shape. This paper presents a novel two-stage liver detection and segmentation model DSL. The first stage uses improved Faster Regions with CNN features (Faster R-CNN) to detect approximate position of liver. The obtained images are processed and input into DeepLab to obtain the contour of liver. The proposed approach is validated on two datasets MICCAI-Sliver07 and 3Dircadb. Experimental results reveal that the proposed method outperforms the state-of-the-art solutions in terms of volume overlap error, average surface distance, relative volume difference, and total score.


Liver segmentation DeepLab Faster R-CNN 



This work was partly supported by the National Nature Science Foundation of China (No. 61309013 and No. 51608070) and Chongqing Basic and frontier research projects (No. CSTC2014JCYJA40042 and No. CSTC2016JCYJA0022).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.College of Computer Science, Chongqing UniversityChongqingChina
  2. 2.Sichuan Changhong Elextronics Co. Ltd.MianyangChina

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