Dr. Albert Comelli
Ri.MED, Palermo, Sicilia, Italy
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12Years (2015-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications26 February 2021 in Journal of Magnetic Resonance Imaging
Journal Article
Renato CuocoloAlbert Comelli
Alessandro StefanoVBViviana Benfante
Viviana Benfante
NDNavdeep Dahiya
Navdeep Dahiya
MIMassimo Imbriaco
Massimo Imbriaco
+4
Journal Article
Albert Comelli
Navdeep DahiyaAlessandro Stefano
Federica VernuccioMPMarzia Portoghese
Marzia PortogheseAYAnthony Yezzi
Anthony Yezzi +3
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.
Journal Article
Muhammad Ali
Viviana BenfanteGBGhazal Basirinia
Ghazal Basirinia
Pierpaolo AlongiASAlessandro Sperandeo
Alessandro SperandeoAlbert Comelli +6
Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.
14 January 2021 in European Radiology
Journal Article
Pierpaolo AlongiAlessandro StefanoAlbert ComelliRLRiccardo Laudicella
Riccardo Laudicella
SSSalvatore Scalisi
Salvatore Scalisi
GRGiorgio Russo
Giorgio Russo
+7
1 April 2016 in Neurotherapeutics
Journal Article
NONicola Orefice
Nicola OreficeMAMireille Alhouayek
Mireille AlhouayekACAntonio Carotenuto
Antonio CarotenutoSMSilvana Montella
Silvana Montella
FBFranscesco Barbato
Franscesco Barbato
GOGiuseppe Orefice
Giuseppe Orefice +3
Journal Article
Muhammad AliViviana Benfante
Domenico Di Raimondo
Giuseppe Salvaggio
Antonino TuttolomondoAlbert ComelliResveratrol is a polyphenolic compound that has gained considerable attention in the past decade due to its multifaceted therapeutic potential, including anti-inflammatory and anticancer properties. However, its anticancer efficacy is impeded by low water solubility, dose-limiting toxicity, low bioavailability, and rapid hepatic metabolism. To overcome these hurdles, various nanoparticles such as organic and inorganic nanoparticles, liposomes, polymeric nanoparticles, dendrimers, solid lipid nanoparticles, gold nanoparticles, zinc oxide nanoparticles, zeolitic imidazolate frameworks, carbon nanotubes, bioactive glass nanoparticles, and mesoporous nanoparticles were employed to deliver resveratrol, enhancing its water solubility, bioavailability, and efficacy against various types of cancer. Resveratrol-loaded nanoparticle or resveratrol-conjugated nanoparticle administration exhibits excellent anticancer potency compared to free resveratrol. This review highlights the latest developments in nanoparticle-based delivery systems for resveratrol, focusing on the potential to overcome limitations associated with the compound’s bioavailability and therapeutic effectiveness.
About Me
Dr. Albert Comelli is currently Senior Scientist in Biomedical Imaging at the Ri.MED Foundation, Palermo, Italy. He received a combined BSc/MSc degree in Computer Science from the University of Catania and a Ph.D. in Computer Engineering from the University of Palermo. He is a long-term research affiliate at the Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù (PA), Italy. His research interests include the development of algorithms for the analysis and classification of biomedical data and images, the identification of new biomarkers through the extraction and selection of radiomic features from images, the implementation of machine learning systems and deep learning for 3D tumor volume segmentation and tissue classification to support medical diagnosis, to develop customized predictive and / or prognostic models to support medical decision making in patients undergoing different methods imaging, management, maintenance and development of experimental protocols for the acquisition of nuclear magnetic resonance (MRI) sequences, computed tomography (CT), positron emission tomography (PET), ultrasound, in vivo imaging system (IVIS), cell images, histological images in clinical and preclinical studies. He is tutor of 10 innovative PhD students and one RTDA with industrial characterization supported by PON R&I 2014-2020 University and Research (MIUR) funds, and INPS Research funds. He is the author of over 85 scientific papers.