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2009
Springer

Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach

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Segmentation of Lung Tumours in Positron Emission Tomography Scans: A Machine Learning Approach
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 ef...
Aliaksei Kerhet, Cormac Small, Harvey Quon, Terenc
Added 24 Feb 2011
Updated 24 Feb 2011
Type Journal
Year 2009
Where AIME
Authors Aliaksei Kerhet, Cormac Small, Harvey Quon, Terence Riauka, Russell Greiner, Alexander McEwan, Wilson Roa
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