2 citations · 3 across the 5 of their papers we have counts for
5 papers
Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors
Ricardo Coimbra Brioso, Damiano Dei, Nicola Lambri +3
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 learnin…
Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs
Leonardo Crespi, Samuele Camnasio, Damiano Dei +4
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 pla…
Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation
Ricardo Coimbra Brioso, Damiano Dei, Nicola Lambri +3
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 acc…
Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net
Ricardo Coimbra Brioso, Damiano Dei, Ciro Franzese +4
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 or…
Ensemble Methods for Multi-Organ Segmentation in CT Series
Leonardo Crespi, Paolo Roncaglioni, Damiano Dei +5
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 advancem…