MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.

BACKGROUND: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. RESULTS: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. CONCLUSIONS: Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans

MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction

Abstract: Background: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. Results: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 ℓ1 loss—trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones—reconstructs unseen healthy/abnormal scans; (Diagnosis) Average ℓ2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. Conclusions: Similar to physicians’ way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans

NODDIにおけるintracellular volume fractionの拡散時間依存性

We investigated time dependence changes of intracellular volume fractin (IVCF) and intraneurite volume fraction (INVF) using oscillationg gradient spin-echo sequence. Both ICVF and INVF decreased with longer diffusion time and showed weak correlation with changes of apparent diffusion coefficient.第45回日本磁気共鳴医学会大

Vers un photo-herbicide marqué ou hydrosoluble bio-inspiré : Synthèse et études de porphyrine en solution et dans les cellules végétales

Au cours de la dernière décennie, l’usage intensif des herbicides en agriculture a provoqué plusieurs crises sanitaires et environnementales. La recherche de nouveaux herbicides bio-inspirés est donc devenue une urgence, en particulier afin de réduire les risques de pollution. Les porphyrines, naturellement présentes dans les végétaux, sont des molécules photosensibles. En présence d’oxygène, leur photo-activation conduit à la production d’Espèces Réactives de l’Oxygène capables d’induire la mort cellulaire. Ce principe utilisé en thérapie photodynamique peut être transféré aux plantes, par exemple à l’aide de porphyrines chargées. Nous avons étudié les propriétés physicochimiques (absorption UV-Visible, émission de fluorescence, photo-stabilité et production d’EROs) ainsi que les effets sur des cellules de tabac TBY-2 d’une série de porphyrines chargées. Pour étudier les mécanismes d’action des porphyrines en tant qu’herbicides, ces molécules doivent être tracées et localisées dans la plante. Dans ce but, nous avons synthétisé des porphyrines liées de manière covalente à un marqueur fluorescent par plusieurs bras espaceurs ; ces derniers ont été choisis grâce à une étude en modélisation moléculaire de leur flexibilité conformationnelle. Les propriétés photo-physiques de ces nouvelles dyades ont été étudiées expérimentalement et théoriquement.Over the past decade, intensive use of herbicide in agriculture has caused several sanitary and environmental problems. Finding new bio-inspired herbicides preventing pollution has appeared crucial. Naturally present in plants, porphyrins are photosensitive. In the presence of oxygen, their photo-activation leads to production of Reactive Oxygen Species, which induce cell death. Already used in Photodynamic Therapy, this effect can be used to plant. In that purpose, a series of charged porphyrins (commercial and synthesized) were selected, and their physicochemical properties (e.g. UV-Vis absorption, fluorescence emission, photostability, ROS production) as well as their effects on TBY-2 (Tobacco Bright Yellow) cells were evaluated. Second, localizing molecules in plants is mandatory to understand mechanisms of the herbicide action. In this context, porphyrins were covalently grafted to a fluorescent marker, by a series of spacers that were chosen according to a preliminary molecular modeling evaluation of their conformational flexibility. The new dyads obtained were thoroughly studied both theoretically and experimentally for their photophysical properties