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 ti...
| Published in: | Medical & Biological Engineering & Computing Vol. 55; no. 1; pp. 127 - 140 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Jan2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120629454&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120629454 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2017 vid: 55 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120629454 120629454 NLM27106756 120629454 10.1007/s11517-016-1495-8 NLM27106756 120629454 ppf: 127 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Metastatic liver tumour segmentation with a neural network-guided 3D deformable model. aug: au: Vorontsov, Eugene Tang, An Roy, David Pal, Christopher Kadoury, Samuel Pal, Christopher J affil: École Polytechnique de Montréal , Montreal Canada sug: subj: Imaging, Three-Dimensional Liver Neoplasms Pathology Neural Networks (Computer) Models, Biological Colorectal Neoplasms Databases Tomography, X-Ray Computed Sensitivity and Specificity Reproducibility of Results Human ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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