To establish the optimal radiotherapy fields for treating brain cancer patients, the tumour volume is often outlined on magnetic resonance (MR) images, where the tumour is clearly visible, and mapped onto computerised tomography images used for radiotherapy planning. This process requires considerable clinical experience and is time consuming, which will continue to increase as more complex image sequences are used in this process. Here, the potential of image analysis techniques for automatically identifying the radiation target volume on MR images, and thereby assisting clinicians with this difficult task, was investigated. A gradient-based level set approach was applied on the MR images of five patients with grades II, III and IV malignant cerebral glioma. The relationship between the target volumes produced by image analysis and those produced by a radiation oncologist was also investigated. The contours produced by image analysis were compared with the contours produced by an oncologist and used for treatment. In 93% of cases, the Dice similarity coefficient was found to be between 60 and 80%. This feasibility study demonstrates that image analysis has the potential for automatic outlining in the management of brain cancer patients, however, more testing and validation on a much larger patient cohort is required.
Keywords: Dice similarity coefficient; MRI; automatic outlining; biomedical MRI; brain; brain cancer; brain cancer patients; cancer; complex image sequences; computerised tomography; computerised tomography images; gradient-based level set approach; image analysis techniques; image sequences; magnetic resonance images; malignant cerebral glioma; medical image processing; oncologist; optimal radiotherapy fields; radiation oncologist; radiation therapy; radiotherapy planning; radiotherapy target volumes; tumour volume; tumours.