Metastatic liver tumour segmentation with a neural network-guided 3D deformable model.
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.
École Polytechnique de Montréal, Montreal, Canada.
Eugene
E
Vorontsov
Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada.|Department of Radiology, Radio-oncology and Nuclear Medicine, Centre hospitalier de l'Université de Montréal (CHUM), Montreal, Canada.
An
A
Tang
École Polytechnique de Montréal, Montreal, Canada.
David
D
Roy
École Polytechnique de Montréal, Montreal, Canada.
Christopher J
CJ
Pal
École Polytechnique de Montréal, Montreal, Canada. samuel.kadoury@polymtl.ca.|CHU Sainte-Justine Research Center, Montreal, Canada. samuel.kadoury@polymtl.ca.
Samuel
S
Kadoury
D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging / Q000556:secondary*; D019992:Databases as Topic; D006801:Humans; D021621:Imaging, Three-Dimensional; D008113:Liver Neoplasms / Q000473:pathology*; D008954:Models, Biological; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed
CT imaging; Deformable surface model; Liver cancer; Multilayer perceptron; Tumour segmentation