0000000000640181
AUTHOR
L. M. Valastro
Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model
Gross Tumor Volume (GTV) segmentation on medical images is an open issue in neuro-radiosurgery. Magnetic Resonance Imaging (MRI) is the most promi-nent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a mini-invasive technique used to deal with inaccessible or insufficiently treated tumors. During the planning phase, the GTV is usually contoured by radiation oncologists using a manual segmentation procedure on MR images. This methodology is certainly time-consuming and op-erator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, is only obtained by using computer-assisted appr…
68 ga-dotatoc pet/ct follow up after single or hypofractionated gamma knife icon radiosurgery for meningioma patients
68Ga-DOTATOC represents a useful tool in tumor contouring for radiosurgery planning. We present a case series of patients affected by meningiomas on who we performed 68Ga-DOTATOC positron emission tomography (PET)/CT pre-operatively, a subgroup of which also underwent a post-operative 68Ga-DOTATOC PET/CT to evaluate the standardized uptake value (SUV) modification after Gamma Knife ICON treatment in single or hypofractionated fractions. Twenty patients were enrolled/included in this study: ten females and ten males. The median age was 52 years (range 33–80). The median tumor diameter was 3.68 cm (range 0.12–22.26 cm), and the median pre-radiotherapy maximum SUV value was 11 (range 2.3–92). …
Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm
Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is…