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1: T1-weighted and T2-weighted axial MR images of an epilepsy patient. The focal cortical dysplasia (red arrows) present as loss of gray-white contrast on T1-weighted imaging and a hyperintensity on T2-weighted imaging. Illustration from [Kini et al., 2016].
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Epilepsy affects around 50 million people worldwide, a third of those diagnosed with medically refractory epilepsy where seizures cannot be controlled by pharmacotherapy. For such patients, surgical resection of the epileptogenic zone may offer a seizure-free life. The success of such surgeries largely depends on the accuracy of the epileptogenic z...
Citations
... Isolation Forest is an ensemble method [45] used as an outlier detector in a variety of datasets [43,46,47]. The algorithm works by isolating each point in the dataset to assess whether the point is an outlier. ...
Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.
... This step is unsupervised, and it depends only on the intrinsic information in the image itself. Then using the normal human sectional anatomy knowledge, radiologists could further tell whether this lesion is critical or not [2]. ...
COVID-19 is a newly identified disease, which is very contagious and has been rapidly spreading across different countries around the world, calling for rapid and accurate diagnosis tools. Chest CT imaging has been widely used in clinical practice for disease diagnosis, but image reading is still a time-consuming work. We aim to integrate an image preprocessing technology for anomaly detection with supervised deep learning for chest CT imaging-based COVID-19 diagnosis. In this study, a matrix profile technique was introduced to CT image anomaly detection in two levels. At one-dimensional level, CT images were simply flatted and transformed to a one-dimensional vector so that the matrix profile algorithm could be implemented for them directly. At two-dimensional level,a matrix profile was calculated in a sliding window way for every segment in the image. An anomaly severity score (CT-SS) was calculated, and the difference of the CT-SS between the COVID-19 CT images and Non-COVID-19 CT images was tested. A sparse anomaly mask was calculated and applied to penalize the pixel values of each image. The anomaly weighted images were then used to train standard DenseNet deep learning models to distinguish the COVID-19 CT from Non-COVID-19 CT images. A VGG19 model was used as a baseline model for comparison. Although extra finetuning needs to be done manually, the one-dimensional matrix profile method could identify the anomalies successfully. Using the two-dimensional matrix profiling method, CT-SS and anomaly weighted image can be successfully generated for each image. The CT-SS significantly differed among the COVID-19 CT images and Non-COVID-19 CT images (
$p-value < 0.05$
). Furthermore, we identified a potential causal association between the number of underlying diseases of a COVID-19 patient and the severity of the disease through statistical mediation analysis. Compared to the raw images, the anomaly weighted images showed generally better performance in training the DenseNet models with different architectures for diagnosing COVID-19, which was validated using two publicly available COVID-19 lung CT image datasets. The metric Area Under the Curve(AUC) on one dataset were 0.7799(weighted)vs. 0.7391(unweighted), 0.7812(weighted) vs. 0.7410(unweighted), 0.7780(weighted) vs. 0.7399(unweighted), 0.7045(weighted) vs. 0.6910(unweighted) for DenseNet121, DenseNet169, DenseNet201, and the baseline model VGG19, respectively. The same trend was observed using another independent dataset. The significant results revealed the critical value of using this existing state-of-the-art algorithm for image anomaly detection. Furthermore, the end-to-end model structure has the potential to work as a rapid tool for clinical imaging-based diagnosis.
Les avancées technologiques émergentes de l’Internet des objets (IoT) ont conduit à une interférence significative des stratégies manufacturières. À cette fin, des concepts tels que "Industrie 4.0", "fabrication intelligente" et "usine numérique" ont vu le jour. Dans ces contextes, la maintenance prédictive joue de plus en plus un rôle crucial dans la réduction des coûts et l’amélioration des performances commerciales car elle utilise des sources de données hétérogènes pour détecter les comportements anormaux des équipements (diagnostic), prédire les modes de défaillance futurs (pronostic) et soutenir les décisions en amont (prise de décision proactive).Dans cette thèse, nous présentons une vue d’ensemble des architectures demaintenance prédictive et nous nous intéressons à un pilier capital de ces architectures, la détection d’anomalies comme première étape de prise de décision dans une architecture de maintenance prédictive. Nous apportons deux contributions à cette question de recherche.Une première méthode de classification semi-supervisée en transport optimal dans deux versions (paramétrique et non-paramétrique) pour la détection d’anomalies dans les séries temporelles. Les travaux expérimentaux de l’application de cette méthode sur des ensembles de données acoustiques synthétiques et réels prouvent la robustesse des métriques au sens transport optimal et démontrent en outre la supériorité des performances de la méthode par rapport aux algorithmes de l’état-de-l’art.La deuxième contribution concerne une méthode non-supervisée de détectiond’anomalies dans des données multidimensionnelles. Elle identifie les valeurs aberrantes locales dans un espace topologique non-euclidien en utilisant des métriques en transport optimal.Les résultats expérimentaux montrent l’efficacité de la méthode à remédier au problème de la malédiction de dimensionnalité et témoignentde la différence statistiquement significative de la méthode proposée par rapportaux méthodes de l’état-de l’art-évaluées.
Although the main structures of cortical folding are present in each human brain, the folding pattern is unique to each individual. Because of this large normal variability, the identification of abnormal patterns associated to developmental disorders is a complex open challenge. In this paper, we tackle this problem as an anomaly detection task and explore the potential of deep generative models using benchmarks made up of synthetic anomalies. To focus learning on the folding geometry, brain MRI are preprocessed first to deal only with a skeleton-based negative cast of the cortex. A variational auto-encoder is trained to get a representation of the regional variability of the folding pattern of the general population. Then several synthetic benchmark datasets of abnormalities are designed. The latent space expressivity is assessed through classification experiments between control’s and abnormal’s latent codes. Finally, the properties encoded in the latent space are analyzed through perturbation of specific latent dimensions and observation of the resulting modification of the reconstructed images. The results have shown that the latent representation is rich enough to distinguish subtle differences like asymmetries between the right and left hemispheres.
Here, we address the hemispheric interdependency of subcortical structures in the aging human brain. In particular, we investigated whether subcortical volume variations can be explained by the adjacency of structures in the same hemisphere or are due to the interhemispheric development of mirror subcortical structures in the brain. Seven subcortical structures in each hemisphere were automatically segmented in a large sample of 3312 magnetic resonance imaging (MRI) studies of elderly individuals in their 70s and 80s. We performed Eigenvalue analysis, and found that anatomic volumes in the limbic system and basal ganglia show similar statistical dependency whether considered in the same hemisphere (intrahemispherically) or different hemispheres (interhemispherically). Our results indicate that anatomic bilaterality of subcortical volumes is preserved in the aging human brain, supporting the hypothesis that coupling between non-adjacent subcortical structures might act as a mechanism to compensate for the deleterious effects of aging.
