Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

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Doshi, Trushali and Soraghan, John and Grose, Derek and MacKenzie, Kenneth and Petropoulakis, Lykourgos; Hadjiiski, Lubomir M. and Tourassi, Georgia D., eds. (2015) Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images. In: Medical Imaging 2015. Proceedings of SPIE . SPIE, USA. ISBN 9781628415049 (https://doi.org/10.1117/12.2081864)

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Abstract

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78±0.04 and average root mean square error of 1.82±0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion.

ORCID iDs

Doshi, Trushali ORCID logoORCID: https://orcid.org/0000-0002-6556-112X, Soraghan, John ORCID logoORCID: https://orcid.org/0000-0003-4418-7391, Grose, Derek, MacKenzie, Kenneth and Petropoulakis, Lykourgos ORCID logoORCID: https://orcid.org/0000-0003-3230-9670; Hadjiiski, Lubomir M. and Tourassi, Georgia D.
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