NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models...

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Autores principales: Iarussi, Emmanuel, Deangeli, Duilio, Külsgaard, Hernán, Iarussi, Francisco, Braggio, Delfina, Princich, Juan Pablo, Bendersky, Mariana, Larrabide, Ignacio, Orlando, José Ignacio
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: Brain Topography 2023
Materias:
Alzheimer Disease
Brain
diagnostic imaging
Novelty detection
Machine learning
Hippocampus
Normal asymmetries
Acceso en línea:https://repositorio.utdt.edu/handle/20.500.13098/11966
https://doi.org/10.1007/s10548-023-00985-6
Aporte de:
Repositorio Digital Universidad Torcuato Di Tella de Universidad Torcuato Di Tella
id I57-R163-20.500.13098-11966
record_format dspace
spelling I57-R163-20.500.13098-119662023-08-08T07:00:17Z NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features Iarussi, Emmanuel Deangeli, Duilio Külsgaard, Hernán Iarussi, Francisco Braggio, Delfina Princich, Juan Pablo Bendersky, Mariana Larrabide, Ignacio Orlando, José Ignacio Alzheimer Disease Brain diagnostic imaging Novelty detection Machine learning Hippocampus Normal asymmetries Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia. La versión final de este artículo fue publicada el 29 de junio de 2023 en Brain Topography (Springer). Se encuentra accesible desde Biblioteca Di Tella a través de Primo 2023-08-02T10:43:56Z 2023-08-02T10:43:56Z 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://repositorio.utdt.edu/handle/20.500.13098/11966 https://doi.org/10.1007/s10548-023-00985-6 eng Este artículo se encuentra publicado desde el 29 de junio de 2023 en Brain Topography (Springer) NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features. Brain Topogr (2023). https://doi.org/10.1007/s10548-023-00985-6 info:eu-repo/semantics/restrictedAccess http://rightsstatements.org/page/InC/1.0/?language=es 17 p. application/pdf application/pdf Brain Topography Springer
institution Universidad Torcuato Di Tella
institution_str I-57
repository_str R-163
collection Repositorio Digital Universidad Torcuato Di Tella
language Inglés
orig_language_str_mv eng
topic Alzheimer Disease
Brain
diagnostic imaging
Novelty detection
Machine learning
Hippocampus
Normal asymmetries
spellingShingle Alzheimer Disease
Brain
diagnostic imaging
Novelty detection
Machine learning
Hippocampus
Normal asymmetries
Iarussi, Emmanuel
Deangeli, Duilio
Külsgaard, Hernán
Iarussi, Francisco
Braggio, Delfina
Princich, Juan Pablo
Bendersky, Mariana
Larrabide, Ignacio
Orlando, José Ignacio
NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features
topic_facet Alzheimer Disease
Brain
diagnostic imaging
Novelty detection
Machine learning
Hippocampus
Normal asymmetries
description Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.
format Artículo
publishedVersion
author Iarussi, Emmanuel
Deangeli, Duilio
Külsgaard, Hernán
Iarussi, Francisco
Braggio, Delfina
Princich, Juan Pablo
Bendersky, Mariana
Larrabide, Ignacio
Orlando, José Ignacio
author_facet Iarussi, Emmanuel
Deangeli, Duilio
Külsgaard, Hernán
Iarussi, Francisco
Braggio, Delfina
Princich, Juan Pablo
Bendersky, Mariana
Larrabide, Ignacio
Orlando, José Ignacio
author_sort Iarussi, Emmanuel
title NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features
title_short NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features
title_full NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features
title_fullStr NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features
title_full_unstemmed NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features
title_sort norha: a normal hippocampal asymmetry deviation index based on one-class novelty detection and 3d shape features
publisher Brain Topography
publishDate 2023
url https://repositorio.utdt.edu/handle/20.500.13098/11966
https://doi.org/10.1007/s10548-023-00985-6
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