Medical & Biological Engineering & Computing

, Volume 55, Issue 1, pp 127–139 | Cite as

Metastatic liver tumour segmentation with a neural network-guided 3D deformable model

  • Eugene Vorontsov
  • An Tang
  • David Roy
  • Christopher J. Pal
  • Samuel Kadoury
Original Article


The segmentation of liver tumours in CT images is useful for the diagnosis and treatment of liver cancer. Furthermore, an accurate assessment of tumour volume aids in the diagnosis and evaluation of treatment response. Currently, segmentation is performed manually by an expert, and because of the time required, a rough estimate of tumour volume is often done instead. We propose a semi-automatic segmentation method that makes use of machine learning within a deformable surface model. Specifically, we propose a deformable model that uses a voxel classifier based on a multilayer perceptron (MLP) to interpret the CT image. The new deformable model considers vertex displacement towards apparent tumour boundaries and regularization that promotes surface smoothness. During operation, a user identifies the target tumour and the mesh then automatically delineates the tumour from the MLP processed image. The method was tested on a dataset of 40 abdominal CT scans with a total of 95 colorectal metastases collected from a variety of scanners with variable spatial resolution. The segmentation results are encouraging with a Dice similarity metric of \(0.80 \pm 0.11\) and demonstrates that the proposed method can deal with highly variable data. This work motivates further research into tumour segmentation using machine learning with more data and deeper neural networks.


Liver cancer Tumour segmentation CT imaging Multilayer perceptron Deformable surface model 



Research funding was supported in part by the Canada Research Chairs and the NSERC Discovery grant programme and the Fonds de recherche du Québec - Santé (FRQS-ARQ #26993).


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Copyright information

© International Federation for Medical and Biological Engineering 2016

Authors and Affiliations

  • Eugene Vorontsov
    • 1
  • An Tang
    • 2
    • 3
  • David Roy
    • 1
  • Christopher J. Pal
    • 1
  • Samuel Kadoury
    • 1
    • 4
  1. 1.École Polytechnique de MontréalMontrealCanada
  2. 2.Centre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM)MontrealCanada
  3. 3.Department of Radiology, Radio-oncology and Nuclear MedicineCentre hospitalier de l’Université de Montréal (CHUM)MontrealCanada
  4. 4.CHU Sainte-Justine Research CenterMontrealCanada

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