Semi-automatic Brain Lesion Segmentation in Gamma Knife Treatments Using an Unsupervised Fuzzy C-Means Clustering Technique

Massimo Midiri, Salvatore Vitabile, Maria Carla Gilardi, Giorgio Russo, Massimo Ippolito, Carmelo Militello, Pietro Pisciotta, Leonardo Rundo, Corrado D’Arrigo, Francesco Marletta

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Neural Networks
Pages15-26
Number of pages12
Publication statusPublished - 2016

Publication series

NameSMART INNOVATION, SYSTEMS AND TECHNOLOGIES

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All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)

Cite this

Midiri, M., Vitabile, S., Gilardi, M. C., Russo, G., Ippolito, M., Militello, C., Pisciotta, P., Rundo, L., D’Arrigo, C., & Marletta, F. (2016). Semi-automatic Brain Lesion Segmentation in Gamma Knife Treatments Using an Unsupervised Fuzzy C-Means Clustering Technique. In Advances in Neural Networks (pp. 15-26). (SMART INNOVATION, SYSTEMS AND TECHNOLOGIES).