Segmentation of Lung Tumours in Positron Emission Tomography Scans: a Machine Learning Approach
- Aliaksei Kerhet, Cross Cancer Institute (PDF)
- Cormac Small, Dept of Radiation Oncology, Cross Cancer Institute
- Terence Riauka, Department of Medical Physics, Cross Cancer Institute
- Russ Greiner, Dept of Computing Science; PI of AICML
- Alexander McEwan, Department of Oncology, University of Alberta
- Wilson Roa
Lung cancer represents the most deadly type of malignancy. In this work we propose a machine learning approach to segmenting lung tumours in Positron Emission Tomography (PET) scans in order to provide a radiation therapist with a "second reader" opinion about the tumour location. For each PET slice, our system extracts a set of attributes, passes them to a trained Support Vector Machine (SVM), and returns the optimal threshold value for distinguishing tumour from healthy voxels in that particular slice. We use this technique to analyse four different PET/CT 3D studies. The system produced fairly accurate segmentation, with Jaccard and Dice's similarity coefficients between 0.82 and 0.98 (the areas outlined by the returned thresholds vs. the ones outlined by the reference thresholds). Besides the high level of geometric similarity, a significant correlation between the returned and the reference thresholds also indicates that during the training phase, the learning algorithm effectively acquired the dependency between the extracted attributes and optimal thresholds.
Citation
A. Kerhet, C. Small, T. Riauka, R. Greiner, A. McEwan, W. Roa. "Segmentation of Lung Tumours in Positron Emission Tomography Scans: a Machine Learning Approach". Artificial Intelligence in Medicine, July 2009.Keywords: | medical, imaging, cancer, PET, machine learning, Support Vector Machine (SVM), Positron Emission Tomography (PET), Radiation Treatment, Lung Cancer, Gross Tumour Volume (GTV), medical informatics |
Category: | In Conference |
BibTeX
@incollection{Kerhet+al:AIME09, author = {Aliaksei Kerhet and Cormac Small and Terence Riauka and Russ Greiner and Alexander McEwan and Wilson Roa}, title = {Segmentation of Lung Tumours in Positron Emission Tomography Scans: a Machine Learning Approach}, booktitle = {Artificial Intelligence in Medicine}, year = 2009, }Last Updated: April 27, 2012
Submitted by Russ Greiner