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

• DOI: 10.1007/s11517-016-1495-8
• Publication Date: 2016-04-22T10:15:38Z
• Authors: Eugene Vorontsov; An Tang; David Roy; Christopher J. Pal; Samuel Kadoury
• Keywords: Liver Neoplasms; Reproducibility of Results; Models, Biological; Sensitivity and Specificity; 3. Good health; 03 medical and health sciences; Imaging, Three-Dimensional; 0302 clinical medicine; Databases as Topic; CT imaging; Tumour segmentation; Multilayer perceptron; Deformable surface model; Humans; Neural Networks, Computer; Colorectal Neoplasms; Tomography, X-Ray Computed; Liver cancer

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 [Formula: see text] 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.