Open AccessA SEGMENTATION PROBLEM IN QUANTITATIVE ASSESSMENT OF ORGAN DISPOSITION IN RADIOTHERAPY
Author(s) -
Giovanni Naldi,
B. Avuzzi,
Simona Fantini,
M. Carrara,
Ester Orlandi,
Elisa Massafra,
Stefano Tomatis
Publication year - 2011
Publication title -
image analysis and stereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.237
H-Index - 27
eISSN - 1854-5165
pISSN - 1580-3139
DOI - 10.5566/ias.v30.p179-186
Subject(s) - segmentation , preprocessor , computer science , artificial intelligence , radiation treatment planning , radiation therapy , pattern recognition (psychology) , medicine , radiology
Radiotherapeutic treatment of cancer is best conducted if the prescription dose is given to the tumor while surrounding normal tissues are maximally spared. With the aim to meet these requirements the complexity of radiotherapy techniques have steadily increased under a strong technological impulse, especially in the last decades. One problem involves the rate of the particular disposition of the structures of interest in a patient. Recently the authors (Tomatis et al., 2010; 2011) have proposed a computational approach in order to represent quantitatively the geometrical features of organs at risk, summarized in characteristics of distance, shape and orientation of such organs in respect to the target. A basic problem to solve before to compute the risk index, is the segmentation of the organs involved in the radiotherapy planning. Here we described a 3D segmentation method by using the clinical computed tomography (CT) data of the patients. Our algorithm is based on different steps, a preprocessing phase where a nonlinear diffusion filter is applied; a level set based method for extract 2D countours; a postprocessing reconstruction of 3D volume from 2D segmented slices. Some comparisons with manually traced segmentation by clinical experts are provided