Semi-automatic Brain Lesion Segmentation in Gamma Knife Treatments Using an Unsupervised Fuzzy C-Means Clustering Technique
- Authors: Rundo, L; Militello, C; Vitabile, S; Russo, G; Pisciotta, P; Marletta, F; Ippolito, M; D'Arrigo, C; Midiri, M; Gilardi, MC;
- Publication year: 2016
- Type: Contributo in atti di convegno pubblicato in volume (Capitolo o saggio)
- OA Link: http://hdl.handle.net/10447/213202
MR Imaging is being increasingly used in radiation treatment planning as well as for staging and assessing tumor response. Leksell Gamma Knife (R) 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.