Adrià Casamitjana

Position | |
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PhD Candidate | adria.casamitjana@upc.edu |
Office | Phone |
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D5-119 | +34 934 011 627 |
Biography
I recieved the degree in Electrical Engineering in 2015 from the Universitat Politècnica de Catalunya (UPC) and M.Sc in Wireless Systems in 2015, from Kungliga Tekniska Högskolan (KTH). I am currently enrolled in a Ph.D program in the Image Processing Group at UPC (GPI), holding a scholarship of the Spanish Government. My main research area is focused on biomedical aplications, with great interest in machine learning and statistics.
I am involved in a project collaboration between the GPI and Fundació Pasqual Maragall (FPM), working on Alzheimer Disease.
Journal Articles top
“Projection to Latent Spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer’s disease”, Frontiers in Neurology, section Applied Neuroimaging, In Press. | ,
“NeAT: a nonlinear analysis toolbox for neuroimaging”, Neuroinformatics, 2020. | ,
“Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge”, IEEE Transactions on Medical Imaging, 2019. | ,
“Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge”, IEEE Transactions on Medical Imaging, 2019. | ,
“Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI”, Alzheimer's Research & Therapy, vol. 11, no. 1, 2019. | ,
Book Chapters and Books top
“Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation”, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017, Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds)., vol. 10670, Cham: Springer, 2018, pp. 381-391. | ,
“Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer’s Disease”, in PRedictive Intelligence in MEdicine, vol. 11121, Springer International Publishing, 2018, pp. 60-67. | ,
“3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures”, in Lecture Notes in Computer Vision, vol. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2017, pp. 150-161. | ,
Conference Papers top
“Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI”, in Alzheimer's Association International Conference, 2019. | ,
“Characteristic Brain Volumetric Changes in the AD Preclinical Signature”, in Alzheimer's Association International Conference, Chicago, USA, 2018. | ,
“Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD”, in Alzheimer's Association International Conference, Chicago, USA, 2018. | ,
“Shared latent structures between imaging features and biomarkers in early stages of Alzheimer's disease”, in Workshop on Predictive Intelligence in Medicine (PRIME), MICCAI, Granada, Spain, 2018. | ,
“Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge”, in MICCAI - Multimodal Brain Tumor Segmentation Challenge, 2018. | ,
Theses top
“Study of early stages of Alzheimer’s disease using magnetic resonance imaging”, Universitat Politècnica de Catalunya, Barcelona, 2019. | ,
Other top
“Brain lesion segmentation using Convolutional Neuronal Networks”. 2018.![]() |
, Ms Thesis |
“Extracción de cráneo en imágenes de resonancia magnética del cerebro utilizando una red neuronal convolucional 3D”. 2017. | ,Ms Thesis |
“Feature Selection Methods for Predicting Pre-Clinical Stage in Alzheimer's Disease”. 2016.![]() |
, Ms Thesis |
Projects top
Research Areas top
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Preclinical Alzheimer's Disease | Internal | Jan 2015 | Dec 2021 |
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Biomedical Applications | Internal | Jan 2012 | Dec 2020 |
Demos and Resources top
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VNeAT (Voxel-wise Neuroimaging Analysis Toolbox) | Software | Jun 2017 |