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2. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

Author

Tangaro, Sabina, Amoroso, Nicola, Brescia, Massimo, Cavuoti, Stefano, Chincarini, Andrea, Errico, Rosangela, Inglese, Paolo, Longo, Giuseppe, Maglietta, Rosalia, Tateo, Andrea, Riccio, Giuseppe, and Bellotti, Roberto

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 features for each voxel (sequential backward elimination) we obtained comparable state of-the-art performances with respect to the standard tool FreeSurfer., Comment: To appear on "Computational and Mathematical Methods in Medicine", Hindawi Publishing Corporation. 19 pages, 7 figures

Published
2015
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3. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author

Allen, Genevera I, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher J, Beaton, Derek, Bellotti, Roberto, Bennett, David A, Boehme, Kevin L, Boutros, Paul C, Caberlotto, Laura, Caloian, Cristian, Campbell, Frederick, Neto, Elias Chaibub, Chang, Yu‐Chuan, Chen, Beibei, Chen, Chien‐Yu, Chien, Ting‐Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard JB, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David W, Friend, Stephen H, Fröhlich, Holger, Gan, Jessica, St George‐Hyslop, Peter, Ghosh, Satrajit S, Glaab, Enrico, Green, Robert C, Guan, Yuanfang, Hong, Ming‐Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Iyappan, Anandhi, Jiang, Qijia, Katsumata, Yuriko, Kauwe, John SK, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mario, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjamin A, Lovestone, Simon, Ma, Tsung‐wei, Malhotra, Ashutosh, Mangravite, Lara M, Maxwell, Taylor J, Merrill, Emily, Nagorski, John, Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Newhouse, Stephen J, Norman, Thea C, Nurtdinov, Ramil N, Oyang, Yen‐Jen, Pawitan, Yudi, Peng, Shengwen, Peters, Mette A, Piccolo, Stephen R, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica Y, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi‐An, Tustison, Nicholas J, Varol, Erdem, Vradenburg, George, Weiner, Michael W, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, and Zhu, Hongtu

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Prevention, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Alzheimer's Disease, Aging, Brain Disorders, Neurological, Alzheimer Disease, Apolipoproteins E, Biomarkers, Cognition Disorders, Computational Biology, Databases, Bibliographic, Humans, Predictive Value of Tests, Azheimer's disease, Crowdsource, Big data, Bioinformatics, Cognitive decline, Imaging, Genetics, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Published
2016

5. An Hippocampal Segmentation Tool Within an Open Cloud Infrastructure

Author

Amoroso, Nicola, Tangaro, Sabina, Errico, Rosangela, Garuccio, Elena, Monda, Anna, Sensi, Francesco, Tateo, Andrea, Bellotti, Roberto, Initiative, [Authorinst]for the Alzheimer’s Disease Neuroimaging, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Murino, Vittorio, editor, Puppo, Enrico, editor, Sona, Diego, editor, Cristani, Marco, editor, and Sansone, Carlo, editor

Published
2015
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11. PSI Clustering for the Assessment of Underground Infrastructure Deterioration

Author

Amoroso, Nicola, primary, Cilli, Roberto, additional, Bellantuono, Loredana, additional, Massimi, Vincenzo, additional, Monaco, Alfonso, additional, Nitti, Davide Oscar, additional, Nutricato, Raffaele, additional, Samarelli, Sergio, additional, Taggio, Niccolò, additional, Tangaro, Sabina, additional, Tateo, Andrea, additional, Guerriero, Luciano, additional, and Bellotti, Roberto, additional

Published
2020
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15. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, Alzheimer's Disease Neuroimaging Initiative, Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, and Alzheimer's Disease Neuroimaging Initiative

Published
2016

16. A hybrid approach for FFP velocity gridding in MPI reconstruction

Author

Tateo, Andrea, primary, Andrisani, Andrea, additional, Iurino, Alessandro, additional, Settanni, Giuseppina, additional, Stifanelli, Patrizia F., additional, Larizza, Pietro, additional, Mazzia, Francesca, additional, Mininni, Rosa Maria, additional, Tangaro, Sabina, additional, and Bellotti, Roberto, additional

Published
2015
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17. Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation

Author

Tangaro, Sabina, primary, Amoroso, Nicola, additional, Brescia, Massimo, additional, Cavuoti, Stefano, additional, Chincarini, Andrea, additional, Errico, Rosangela, additional, Inglese, Paolo, additional, Longo, Giuseppe, additional, Maglietta, Rosalia, additional, Tateo, Andrea, additional, Riccio, Giuseppe, additional, and Bellotti, Roberto, additional

Published
2015
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18. Ensemble using different Planetary Boundary Layer schemes in WRF model for wind speed and direction prediction over Apulia region.

Author

Tateo, Andrea, Miglietta, Mario Marcello, Fedele, Francesca, Menegotto, Micaela, Monaco, Alfonso, and Bellotti, Roberto

Subjects
*ATMOSPHERIC boundary layer, *METEOROLOGICAL research
Abstract

The Weather Research and Forecasting mesoscale model (WRF) was used to simulate hourly 10m wind speed and direction over the city of Taranto, Apulia region (south-eastern Italy). This area is characterized by a large industrial complex including the largest European steel plant and is subject to a Regional Air Quality Recovery Plan. This plan constrains industries in the area to reduce by 10% the mean daily emissions by diffuse and point sources during specific meteorological conditions named wind days. According to the Recovery Plan, the Regional Environmental Agency ARPA-PUGLIA is responsible for forecasting these specific meteorological conditions with 72 h in advance and possibly issue the early warning. In particular, an accurate wind simulation is required. Unfortunately, numerical weather prediction models suffer from errors, especially for what concerns near-surface fields. These errors depend primarily on uncertainties in the initial and boundary conditions provided by global models and secondly on the model formulation, in particular the physical parametrizations used to represent processes such as turbulence, radiation exchange, cumulus and microphysics. In our work, we tried to compensate for the latter limitation by using different Planetary Boundary Layer (PBL) parameterization schemes. Five combinations of PBL and Surface Layer (SL) schemes were considered. Simulations are implemented in a real-time configuration since our intention is to analyze the same configuration implemented by ARPA-PUGLIA for operational runs; the validation is focused over a time range extending from 49 to 72 h with hourly time resolution. The assessment of the performance was computed by comparing the WRF model output with ground data measured at a weather monitoring station in Taranto, near the steel plant. After the analysis of the simulations performed with different PBL schemes, both simple (e.g. average) and more complex post-processing methods (e.g. weighted average, linear and nonlinear regression, and artificial neural network) are adopted to improve the performances with respect to the output of each single setup. The neural network approach comes out as the most promising method. [ABSTRACT FROM AUTHOR]

Published
2017
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20. Effetto di un esoscheletro indossabile sulla funzione deambulatoria in pazienti con esiti di polineuropatia infiammatoria periferica: protocollo di uno studio pilota.

Author

Tateo, Andrea, thesis supervisor: Turolla, Andrea, Tateo, Andrea, and thesis supervisor: Turolla, Andrea

Abstract

Background: le polineuropatie infiammatorie periferiche sono un gruppo di patologie eterogenee caratterizzate da lesioni o disfunzioni di molteplici nervi periferici e radici nervose, con il coinvolgimento di mielina o assoni. Queste comportano alterazioni dei ROT, deficit sensitivi ed ipostenia simmetrica prevalentemente distale. Il cammino è una delle funzioni più colpite da queste patologie e nel 20% dei pazienti possono residuare deficit agli arti inferiori, che possono causare disabilità. Obiettivi: l’obiettivo primario dello studio è di valutare l’effetto di un protocollo riabilitativo con esoscheletro indossabile sul recupero della funzione deambulatoria, dell’equilibrio e della funzionalità degli arti inferiori in pazienti con esiti di polineuropatia infiammatoria periferica. Gli obiettivi secondari dello studio sono: confrontare gli outcomes del gruppo sperimentale con quelli ottenuti da un protocollo riabilitativo standard, come da normale pratica clinica; valutare la soddisfazione degli utenti rispetto allo svolgimento di un programma riabilitativo svolto con esoscheletro. Metodi: Un campione di 38 pazienti verrà arruolato su base volontaria e randomizzato in un gruppo sperimentale ed uno di controllo. Nel primo il programma riabilitativo verrà effettuato con esoscheletro indossabile, mentre nel secondo si svolgerà il programma riabilitativo come da normale pratica clinica. Al termine dello studio i risultati delle valutazioni effettuate nei due gruppi verranno confrontati in tre tempi (T0: una settimana prima dell’intervento; T1: immediatamente al termine dell’intervento; T2: follow-up a 3 mesi). Discussione: l’utilizzo di tecnologie robotiche per la riabilitazione di deficit neurologici è in continua crescita. Tuttavia, non vi sono studi che ne indaghino l’effetto in questa popolazione. Risultati positivi permetterebbero di strutturare studi futuri più ampi, con adeguata potenza campionaria per lo sviluppo di protocolli riabilitativi di tali patologie.

21. Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation

Author

Roberto Bellotti, Massimo Brescia, Rosalia Maglietta, Rosangela Errico, Nicola Amoroso, Paolo Inglese, Stefano Cavuoti, Giuseppe Longo, Giuseppe Riccio, Sabina Tangaro, Andrea Chincarini, Andrea Tateo, Tangaro, Sabina, Amoroso, N., Brescia, M., Cavuoti, S., Chincarini, A., Errico, R., Inglese, P., Longo, G., Maglietta, R., Tateo, A., Riccio, G., Bellotti, R., Amoroso, Nicola, Brescia, Massimo, Cavuoti, Stefano, Chincarini, Andrea, Errico, Rosangela, Paolo, Inglese, Longo, Giuseppe, Maglietta, Rosalia, Tateo, Andrea, Riccio, Giuseppe, Bellotti, Roberto, and ITA

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Computer Vision and Pattern Recognition (cs.CV), Physics - Medical Physic, Computer Science - Computer Vision and Pattern Recognition, computer.software_genre, Hippocampus, Machine Learning (cs.LG), Pattern Recognition, Automated, Machine Learning, Voxel, Image Processing, Computer-Assisted, Physic, Medical Physic, medicine.diagnostic_test, Applied Mathematics, General Medicine, Magnetic Resonance Imaging, Random forest, Feature (computer vision), Modeling and Simulation, lcsh:R858-859.7, Computer Vision and Pattern Recognition, MILD COGNITIVE IMPAIRMENT, MAMMOGRAPHIC DATABASE, ALZHEIMERS-DISEASE, VALIDATION, CLASSIFICATION, MRI, Research Article, Article Subject, FOS: Physical sciences, Feature selection, lcsh:Computer applications to medicine. Medical informatics, General Biochemistry, Genetics and Molecular Biology, Set (abstract data type), medicine, Learning, Humans, General Immunology and Microbiology, business.industry, Computational Biology, Pattern recognition, Magnetic resonance imaging, Filter (signal processing), Physics - Medical Physics, Computer Science - Learning, Independent set, Computer Science, Artificial intelligence, Medical Physics (physics.med-ph), business, computer
Abstract

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 features for each voxel (sequential backward elimination) we obtained comparable state of-the-art performances with respect to the standard tool FreeSurfer., Comment: To appear on "Computational and Mathematical Methods in Medicine", Hindawi Publishing Corporation. 19 pages, 7 figures

Published
2015

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