MR Imaging is being increasingly used in radiation treatment planning as well as for staging and assessing tumor response. Leksell Gamma Knife® is a device for stereotactic neuro-radiosurgery to deal with inaccessible or insufficiently treated lesions with traditional surgery or radiotherapy. The target to be treated with radiation beams is currently contoured through slice-by-slice manual segmentation on MR images. This procedure is time consuming and operator-dependent. Segmentation result repeatability may be ensured only by using automatic/semi-automatic methods with the clinicians supporting the planning phase. In this paper a semi-automatic segmentation method, based on an unsupervised Fuzzy C-Means clustering technique, is proposed. The presented approach allows for the target segmentation and its volume calculation. Segmentation tests on 5 MRI series were performed, using both area-based and distance-based metrics. The following average values have been obtained: DS = 95.10, JC = 90.82, TPF = 95.86, FNF = 2.18, MAD = 0.302, MAXD = 1.260, H = 1.636.
CITATION STYLE
Rundo, L., Militello, C., Vitabile, S., Russo, G., Pisciotta, P., Marletta, F., … Gilardi, M. C. (2016). Semi-automatic brain lesion segmentation in gamma knife treatments using an unsupervised fuzzy c-means clustering technique. In Smart Innovation, Systems and Technologies (Vol. 54, p. 15). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-33747-0_2
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