Sotiras, Aristeidis

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data

Radiomics and Machine Learning in Medical ImagingBrain Tumor Detection and Classification

Cited by 3,049

Deformable medical image registration: a survey

Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Radiotherapy Techniques

Cited by 1,587

Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017) · The Cancer Imaging Archive

Brain Tumor Detection and ClassificationRadiomics and Machine Learning in Medical Imaging

Cited by 316

Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017) · The Cancer Imaging Archive

Brain Tumor Detection and ClassificationRadiomics and Machine Learning in Medical Imaging

Cited by 290

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

Davatzikos, C., Rathore, S., Bakas, S., Pati, S., Bergman, M., Kalarot, R., Sridharan, P., Gastounioti, A., Jahani, N., Cohen, E., et al. (2018) · Journal of Medical Imaging

Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis

Cited by 204

The Brain Chart of Aging: Machine‐learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

Habes, M., Pomponio, R., Shou, H., Doshi, J., Mamourian, E., Erus, G., Nasrallah, I., Launer, L. J., Rashid, T., Bilgel, M., et al. (2021) · Alzheimer's & Dementia

Dementia and Cognitive Impairment ResearchCerebrovascular and genetic disorders

Cited by 173

Evidence for dissociable linkage of dimensions of psychopathology to brain structure in youths

Kaczkurkin, A. N., Park, S. S., Sotiras, A., Moore, T. M., Calkins, M. E., Cieslak, M., Rosen, A. F. G., Ciric, R., Xia, C. H., Cui, Z., et al. (2019) · American Journal of Psychiatry

Functional Brain Connectivity StudiesMental Health Research Topics

Cited by 110

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation

Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., & Davatzikos, C. (2016) · Lecture Notes in Computer Science

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 101

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Pati, S., Singh, A., Rathore, S., Gastounioti, A., Bergman, M., Ngo, P., Ha, S. M., Bounias, D., Minock, J., Murphy, G., et al. (2020) · Lecture Notes in Computer Science

Radiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

Cited by 94

Diminished Cortical Thickness Is Associated with Impulsive Choice in Adolescence

Pehlivanova, M., Wolf, D. H., Sotiras, A., Kaczkurkin, A. N., Moore, T. M., Ciric, R., Cook, P. A., Garcia de La Garza, A., Rosen, A. F. G., Ruparel, K., et al. (2018) · The Journal of Neuroscience

Neural and Behavioral Psychology StudiesDecision-Making and Behavioral Economics

Cited by 79

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Marinescu, R. V., Oxtoby, N. P., Young, A. L., Bron, E. E., Toga, A. W., Weiner, M. W., Barkhof, F., Fox, N. C., Eshaghi, A., Toni, T., et al. (2021) · Machine Learning for Biomedical Imaging

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 68

Genetic and clinical correlates of AI-based brain aging patterns in cognitively unimpaired individuals

Skampardoni, I., Nasrallah, I. M., Abdulkadir, A., Wen, J., Melhem, R., Mamourian, E., Erus, G., Doshi, J., Singh, A., Yang, Z., et al. (2024) · JAMA Psychiatry

Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity Studies

Cited by 28

Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach

Sun, D., Adduru, V. R., Phillips, R. D., Bouchard, H. C., Sotiras, A., Michael, A. M., Baker, F. C., Tapert, S. F., Brown, S. A., Clark, D. B., et al. (2023) · Neuropsychopharmacology

Substance Abuse Treatment and OutcomesNeural and Behavioral Psychology Studies

Cited by 18

QCResUNet: Joint Subject-Level and Voxel-Level Prediction of Segmentation Quality

Qiu, P., Chakrabarty, S., Nguyen, P., Ghosh, S. S., & Sotiras, A. (2023) · Lecture Notes in Computer Science

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 3