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Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

  • Patrick Ferdinand ChristEmail author
  • Mohamed Ezzeldin A. Elshaer
  • Florian Ettlinger
  • Sunil Tatavarty
  • Marc Bickel
  • Patrick Bilic
  • Markus Rempfler
  • Marco Armbruster
  • Felix Hofmann
  • Melvin D’Anastasi
  • Wieland H. Sommer
  • Seyed-Ahmad Ahmadi
  • Bjoern H. Menze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs). We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions from the predicted liver ROIs of step 1. We refine the segmentations of the CFCN using a dense 3D CRF that accounts for both spatial coherence and appearance. CFCN models were trained in a 2-fold cross-validation on the abdominal CT dataset 3DIRCAD comprising 15 hepatic tumor volumes. Our results show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over \(94\,\%\) for liver with computation times below 100 s per volume. We experimentally demonstrate the robustness of the proposed method as a decision support system with a high accuracy and speed for usage in daily clinical routine.

Keywords

Liver Lesion Segmentation FCN CRF CFCN Deep learning 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Patrick Ferdinand Christ
    • 1
    Email author
  • Mohamed Ezzeldin A. Elshaer
    • 1
  • Florian Ettlinger
    • 1
  • Sunil Tatavarty
    • 2
  • Marc Bickel
    • 1
  • Patrick Bilic
    • 1
  • Markus Rempfler
    • 1
  • Marco Armbruster
    • 4
  • Felix Hofmann
    • 4
  • Melvin D’Anastasi
    • 4
  • Wieland H. Sommer
    • 4
  • Seyed-Ahmad Ahmadi
    • 3
  • Bjoern H. Menze
    • 1
  1. 1.Image-Based Biomedical Modeling GroupTechnische Universität MünchenMunichGermany
  2. 2.Chair for Data ProcessingTechnische Universität MünchenMunichGermany
  3. 3.Department for NeurologyLMU Hospital GrosshadernMunichGermany
  4. 4.Department for Clinical RadiologyLMU Hospital GrosshadernMunichGermany

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