Cascade U-ResNets for Simultaneous Liver and Lesion Segmentation

Xue Feng Xi, Lei Wang, Victor S. Sheng, Zhiming Cui, Baochuan Fu, Fuyuan Hu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In recent years, several deep learning networks are proposed to segment 2D or 3D bio-medical images. However, in liver and lesion segmentation, the proportion of interested tissues and lesions are tiny when contrasting to the image background. That is, the objects to be segmented are highly imbalanced in terms of the frequency of occurrences. This makes existing deep learning networks prone to predict pixels of livers and lesions as background. To address this imbalance issue, several loss functions are proposed. Since no researches are having made a comparison among those proposed loss functions, we are curious about that which loss function is the best among them? At the same time, we also want to investigate whether the combination of several different loss functions is effective for liver and lesion segmentation. Firstly, we propose a novel deep learning network (cascade U-ResNets) to produce liver and lesion segmentation simultaneously. Then, we investigate the performance of 5 selected loss functions, WCE (Weighted Cross Entropy), DL (Dice Loss), WDL (Weighted Dice Loss), TL (Teverskry Loss), WTL (Weighted Teversky Loss), with our cascade U-ResNets. We further assemble all cascade U-ResNets trained with different loss functions together to segment livers and lesions jointly on the liver CT (Computed Tomography) volume. Experimental results on the LiTS dataset1 showed our ensemble model can achieve much better results than every individual model for liver segmentation.1http://competitions.codalab.org/competitions/17094#

Original languageEnglish
Article number9057545
Pages (from-to)68944-68952
Number of pages9
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Data imbalance
  • deep learning
  • ensemble learning
  • lesion segmentation
  • liver segmentation
  • medical image segmentation

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