Paolo Zaffino

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PURPOSE Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation(More)
PURPOSE To improve the contouring of clinical target volume for the radiotherapy of neck Hodgkin/non-Hodgkin lymphoma by localizing the prechemotherapy gross target volume onto the simulation computed tomography using [18F]-fluorodeoxyglucose positron emission tomography/computed tomography. MATERIAL AND METHODS The gross target volume delineated on(More)
PURPOSE To obtain a contrasted image of the tumor region during the setup for proton therapy in lung patients, by using proton radiography and x-ray computed tomography (CT) prior knowledge. METHODS AND MATERIALS Six lung cancer patients' CT scans were preprocessed by masking out the gross tumor volume (GTV), and digitally reconstructed radiographs along(More)
OBJECTIVES Atlas-based automatic segmentation (ABAS) addresses the challenges of accuracy and reliability in manual segmentation. We aim to evaluate the contribution of specific-purpose in ABAS of breast cancer (BC) patients with respect to generic-purpose libraries. MATERIALS AND METHODS One generic-purpose and 9 specific-purpose libraries, stratified(More)
PURPOSE Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this(More)
PURPOSE In this work we present the validation of Plastimatch MABS, an open source software for multi atlas based segmentation of medical images. METHODS The validation was performed on two different clinical datasets: 1) 25 CT image volumes of patients treated for H&N cancer; 2) 20 MRI series of patients having a neurological diagnosis. For the first(More)
Atlas-based segmentation is a frequently used approach in medical imaging and multi atlas-based segmentation (MABS) has achieved great success for various applications. In order to simultaneously exploit the capabilities of MABS, limit execution time and maintain robustness, it is preferable to select a (preferably small) subset of atlases to be used for(More)
PURPOSE Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased(More)
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