Výsledky vyhledávání - "Dei, Damiano"

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Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors

Autor: Brioso, Ricardo Coimbra, Dei, Damiano, Lambri, Nicola, Mancosu, Pietro, Scorsetti, Marta, Loiacono, Daniele

Radiotherapy requires precise segmentation of organs at risk (OARs) and of the Clinical Target Volume (CTV) to maximize treatment efficacy and minimize toxicity. While deep learning (DL) has significantly advanced automatic contouring, complex target

Externí odkaz: http://arxiv.org/abs/2409.15888

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Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs

Autor: Crespi, Leonardo, Camnasio, Samuele, Dei, Damiano, Lambri, Nicola, Mancosu, Pietro, Scorsetti, Marta, Loiacono, Daniele

In many clinical settings, the use of both Computed Tomography (CT) and Magnetic Resonance (MRI) is necessary to pursue a thorough understanding of the patient's anatomy and to plan a suitable therapeutical strategy; this is often the case in MRI-bas

Externí odkaz: http://arxiv.org/abs/2407.10888

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Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation

Autor: Brioso, Ricardo Coimbra, Dei, Damiano, Lambri, Nicola, Loiacono, Daniele, Mancosu, Pietro, Scorsetti, Marta

In order to optimize the radiotherapy delivery for cancer treatment, especially when dealing with complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), the accurate contouring of the Planning Target Volume (PTV) is crucial. Unfor

Externí odkaz: http://arxiv.org/abs/2402.06494

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Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net

Autor: Brioso, Ricardo Coimbra, Dei, Damiano, Franzese, Ciro, Lambri, Nicola, Loiacono, Daniele, Mancosu, Pietro, Scorsetti, Marta

Radiotherapy (RT) is a key component in the treatment of various cancers, including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Precise delineation of organs at risk (OARs) and target areas is essential for effective treatm

Externí odkaz: http://arxiv.org/abs/2304.02353

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Ensemble Methods for Multi-Organ Segmentation in CT Series

Autor: Crespi, Leonardo, Roncaglioni, Paolo, Dei, Damiano, Franzese, Ciro, Lambri, Nicola, Loiacono, Daniele, Mancosu, Pietro, Scorsetti, Marta

In the medical images field, semantic segmentation is one of the most important, yet difficult and time-consuming tasks to be performed by physicians. Thanks to the recent advancement in the Deep Learning models regarding Computer Vision, the promise

Externí odkaz: http://arxiv.org/abs/2303.17956

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