TY - GEN
T1 - Structured outlier detection in neuroimaging studies with minimal convex polytopes
AU - Varol, Erdem
AU - Sotiras, Aristeidis
AU - Davatzikos, Christos
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Computer assisted imaging aims to characterize disease processes by contrasting healthy and pathological populations. The sensitivity of these analyses is hindered by the variability in the neuroanatomy of the normal population. To alleviate this shortcoming,it is necessary to define a normative range of controls. Moreover,elucidating the structure in outliers may be important in understanding diverging individuals and characterizing prodromal disease states. To address these issues,we propose a novel geometric concept called minimal convex polytope (MCP). The proposed approach is used to simultaneously capture high probability regions in datasets consisting of normal subjects,and delineate outliers,thus characterizing the main directions of deviation from the normative range. We validated our method using simulated datasets before applying it to an imaging study of elderly subjects consisting of 177 controls,123 Alzheimer’s disease (AD) and 285 mild cognitive impairment (MCI) patients. We show that cerebellar degeneration is a major type of deviation among the controls. Furthermore,our findings suggest that a subset of AD patients may be following an accelerated type of deviation that is observed among the normal population.
AB - Computer assisted imaging aims to characterize disease processes by contrasting healthy and pathological populations. The sensitivity of these analyses is hindered by the variability in the neuroanatomy of the normal population. To alleviate this shortcoming,it is necessary to define a normative range of controls. Moreover,elucidating the structure in outliers may be important in understanding diverging individuals and characterizing prodromal disease states. To address these issues,we propose a novel geometric concept called minimal convex polytope (MCP). The proposed approach is used to simultaneously capture high probability regions in datasets consisting of normal subjects,and delineate outliers,thus characterizing the main directions of deviation from the normative range. We validated our method using simulated datasets before applying it to an imaging study of elderly subjects consisting of 177 controls,123 Alzheimer’s disease (AD) and 285 mild cognitive impairment (MCI) patients. We show that cerebellar degeneration is a major type of deviation among the controls. Furthermore,our findings suggest that a subset of AD patients may be following an accelerated type of deviation that is observed among the normal population.
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U2 - 10.1007/978-3-319-46720-7_35
DO - 10.1007/978-3-319-46720-7_35
M3 - Conference contribution
AN - SCOPUS:84996487049
SN - 9783319467191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 300
EP - 307
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Ourselin, Sebastian
A2 - Joskowicz, Leo
A2 - Sabuncu, Mert R.
A2 - Wells, William
A2 - Unal, Gozde
PB - Springer Verlag
T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -