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

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Changhee Han ◽  
Leonardo Rundo ◽  
Kohei Murao ◽  
Tomoyuki Noguchi ◽  
Yuki Shimahara ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Anomaly Detection ◽  
Late Stage ◽  
Large Scale ◽  
Early Stage ◽  
Brain Mri ◽  
Disease Diagnosis ◽  
Training Data ◽  
Mri Scans ◽  
Adjacent Brain

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 $$\ell _1$$ ℓ 1 loss—trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones—reconstructs unseen healthy/abnormal scans; (Diagnosis) Average $$\ell _2$$ ℓ 2 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.

Metagenomic and Metatranscriptomic Responses of Chemical Dispersant Application during a Marine Dilbit Spill

2022 ◽  
Author(s):  
Yiqi Cao ◽  
Baiyu Zhang ◽  
Charles W. Greer ◽  
Kenneth Lee ◽  
Qinhong Cai ◽  
...  
Keyword(s):  
Microbial Community ◽  
Late Stage ◽  
Large Scale ◽  
Community Dynamics ◽  
Early Stage ◽  
Small Scale ◽  
Rrna Gene ◽  
Shotgun Metagenomics ◽  
Microcosm Studies ◽  
Over Time

The global increase in marine transportation of dilbit (diluted bitumen) can increase the risk of spills, and the application of chemical dispersants remains a common response practice in spill events. To reliably evaluate dispersant effects on dilbit biodegradation over time, we set large-scale (1500 mL) microcosms without nutrients addition using low dilbit concentration (30 ppm). Shotgun metagenomics and metatranscriptomics were deployed to investigate microbial community responses to naturally and chemically dispersed dilbit. We found that the large-scale microcosms could produce more reproducible community trajectories than small-scale (250 mL) ones based on the 16S rRNA gene amplicon sequencing. In the early-stage large-scale microcosms, multiple genera were involved into the biodegradation of dilbit, while dispersant addition enriched primarily Alteromonas and competed for the utilization of dilbit, causing depressed degradation of aromatics. The metatranscriptomic based Metagenome Assembled Genomes (MAG) further elucidated early-stage microbial antioxidation mechanism, which showed dispersant addition triggered the increased expression of the antioxidation process genes of Alteromonas species. Differently, in the late stage, the microbial communities showed high diversity and richness and similar compositions and metabolic functions regardless of dispersant addition, indicating the biotransformation of remaining compounds can occur within the post-oil communities. These findings can guide future microcosm studies and the application of chemical dispersants for responding to a marine dilbit spill. Importance In this study, we employed microcosms to study the effects of marine dilbit spill and dispersant application on microbial community dynamics over time. We evaluated the impacts of microcosm scale and found that increasing the scale is beneficial for reducing community stochasticity, especially in the late stage of biodegradation. We observed that dispersant application suppressed aromatics biodegradation in the early stage (6 days) whereas exerting insignificant effects in the late stage (50 days), from both substances removal and metagenomic/metatranscriptomic perspectives. We further found that Alteromonas species are vital for the early-stage chemically dispersed oil biodegradation, and clarified their degradation and antioxidation mechanisms. The findings would help to better understand microcosm studies and microbial roles for biodegrading dilbit and chemically dispersed dilbit, and suggest that dispersant evaluation in large-scale systems and even through field trails would be more realistic after marine oil spill response.

scholarly journals Harmonization of large multi-site imaging datasets: Application to 10,232 MRIs for the analysis of imaging patterns of structural brain change throughout the lifespan

2019 ◽  
Author(s):  
Raymond Pomponio ◽  
Guray Erus ◽  
Mohamad Habes ◽  
Jimit Doshi ◽  
Dhivya Srinivasan ◽  
...  
Keyword(s):  
Brain Structure ◽  
Large Scale ◽  
Brain Mri ◽  
Big Data Analytics ◽  
Imaging Data ◽  
Mri Scans ◽  
Age Trends ◽  
Development And Aging ◽  
Structural Brain Change ◽  
Structural Brain

AbstractAs medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,232 structural brain MRI scans from participants without known neuropsychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive normative age trends of brain structure through the lifespan (3 to 96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this normative reference of brain development and aging, and to examine deviations from normative ranges, potentially related to disease.

scholarly journals Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

2021 ◽  
Vol 11 (19) ◽  
pp. 9290
Author(s):  
Jaeyong Kang ◽  
Chul-Su Kim ◽  
Jeong Won Kang ◽  
Jeonghwan Gwak
Keyword(s):  
Anomaly Detection ◽  
Short Term Memory ◽  
Detection System ◽  
Early Stage ◽  
Absolute Error ◽  
Training Data ◽  
Pressure Data ◽  
The One ◽  
Maintenance And Inspection ◽  
Operating Unit

Detecting anomalies in the Brake Operating Unit (BOU) braking system of metro trains is very important for trains’ reliability and safety. However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. First, we extracted brake cylinder (BC) pressure data from the BOU data since one of the anomaly cases of metro trains is that BC pressure relief time is delayed by 4 s. After that, extracted BC pressure data is split into subsequences which are fed into our proposed one-class LSTM autoencoder which consists of two LSTM blocks (encoder and decoder). The one-class LSTM autoencoder is trained using training data which only consists of normal subsequences. To detect anomalies from test data that contain abnormal subsequences, the mean absolute error (MAE) for each subsequence is calculated. When the error is larger than a predefined threshold which was set to the maximum value of MAE in the training (normal) dataset, we can declare that example an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental results show that our proposed method can detect anomalies of the BOU data well.

scholarly journals Morphometricity as a measure of the neuroanatomical signature of a trait

2016 ◽  
Vol 113 (39) ◽  
pp. E5749-E5756 ◽  
Author(s):  
Mert R. Sabuncu ◽  
Tian Ge ◽  
Avram J. Holmes ◽  
Jordan W. Smoller ◽  
Randy L. Buckner ◽  
...  
Keyword(s):  
Large Scale ◽  
Anatomical Variation ◽  
Brain Mri ◽  
Mixed Effects ◽  
Brain Morphology ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Behavioral Traits ◽  
Clinical Diagnoses ◽  
Mri Scans

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.

scholarly journals Progressive Temporal-Spatial-Semantic Analysis of Driving Anomaly Detection and Recounting

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5098 ◽  
Author(s):  
Rixing Zhu ◽  
Jianwu Fang ◽  
Hongke Xu ◽  
Jianru Xue
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Semantic Analysis ◽  
Training Data ◽  
Superior Performance ◽  
Video Clips ◽  
Effective Performance ◽  
Temporal Aspect ◽  
Traffic Anomaly ◽  
Coarse To Fine

For analyzing the traffic anomaly within dashcam videos from the perspective of ego-vehicles, the agent should spatial-temporally localize the abnormal occasion and regions and give a semantically recounting of what happened. Most existing formulations concentrate on the former spatial-temporal aspect and mainly approach this goal by training normal pattern classifiers/regressors/dictionaries with large-scale availably labeled data. However, anomalies are context-related, and it is difficult to distinguish the margin of abnormal and normal clearly. This paper proposes a progressive unsupervised driving anomaly detection and recounting (D&R) framework. The highlights are three-fold: (1) We formulate driving anomaly D&R as a temporal-spatial-semantic (TSS) model, which achieves a coarse-to-fine focusing and generates convincing driving anomaly D&R. (2) This work contributes an unsupervised D&R without any training data while performing an effective performance. (3) We novelly introduce the traffic saliency, isolation forest, visual semantic causal relations of driving scene to effectively construct the TSS model. Extensive experiments on a driving anomaly dataset with 106 video clips (temporal-spatial-semantically labeled carefully by ourselves) demonstrate superior performance over existing techniques.

Targeted ShRNA Screening Identified Critical Role of Pleckstrin-2 in Erythropoiesis

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3199-3199
Author(s):  
Ganesan Keerthivasan ◽  
Jing Yang ◽  
Piu Wong ◽  
John Doench ◽  
David E. Root ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Late Stage ◽  
Large Scale ◽  
Conflicts Of Interest ◽  
Early Stage ◽  
Critical Role ◽  
Erythroid Cells ◽  
Novel Genes ◽  
Erythroid Precursor ◽  
In Erythroid Cells

Abstract Abstract 3199 Mammalian erythropoiesis is globally regulated by erythropoietin (Epo). Epo binds to its receptor on the cell surface of erythroid precursor; induces a series of downstream pathways that promote cell differentiation and inhibit apoptosis. Recent genome wide transcriptional profile study demonstrated that over 500 genes are up-regulated during erythropoiesis. Many of these genes encode erythroid specific proteins that play well-known functions in red cells. However, the functions of the most other genes in the erythroid cells are still unknown. To identify novel genes in erythropoiesis, we infected mouse fetal liver erythroblasts with lentiviruses containing mammalian shRNA knockdown library that selectively includes the most highly upregulated 100 genes with unknown functions in erythroid cells. The infected cells were cultured in two different conditions for the characterization of early and late stage erythropoiesis using a high throughput flow cytometry based analysis. With these methods, we identified 33 novel genes that regulate cell differentiation or apoptosis in early stage erythropoisis; 20 genes play important roles in late stage erythropoiesis including enucleation. Significantly, there is an overlap of 16 genes that function in both early and late stage erythropoiesis. We focused on pleckstrin-2, which is specifically and abundantly expressed in erythroid cells, to further characterize its detailed functions in red cell development. We found that knockdown of pleckstrin-2 leads to dramatic apoptosis in early stage erythropoiesis. Knockdown of pleckstrin-2 in late stage erythropoiesis blocks enucleation with no apparent effects on cell differentiation, proliferation or apoptosis. We further discovered that pleckstrin-2 deficiency in early and late erythroblasts disrupts normal actin cytoskeleton as evidenced by super-resolution immunofluorescence microscope. To elucidate the detailed mechanisms of the functions of pleckstrin-2 in different stages of erythropoiesis, we performed proteomic studies and identified candidate proteins that interact with pleckstrin-2 that may contribute to the phenotypes of apoptosis and enucleation defects. In summary, our study identified pleckstrin-2 as a critical regulator of mammalian erythropoiesis and proved the significance of large-scale shRNA screening in the discovery of novel genes in development. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN

2019 ◽  
Vol 8 (4) ◽  
pp. 191 ◽  
Author(s):  
Philipp Schuegraf ◽  
Ksenia Bittner
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Early Stage ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Training Data ◽  
Spectral Information ◽  
Surface Model

Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatically extract building footprints. The integration of different information, which is presently achievable due to the availability of high-resolution remote sensing data sources, makes it possible to improve the quality of the extracted building outlines. Recently, deep neural networks were extended from image-level to pixel-level labelling, allowing to densely predict semantic labels. Based on these advances, we propose an end-to-end U-shaped neural network, which efficiently merges depth and spectral information within two parallel networks combined at the late stage for binary building mask generation. Moreover, as satellites usually provide high-resolution panchromatic images, but only low-resolution multi-spectral images, we tackle this issue by using a residual neural network block. It fuses those images with different spatial resolution at the early stage, before passing the fused information to the Unet stream, responsible for processing spectral information. In a parallel stream, a stereo digital surface model (DSM) is also processed by the Unet. Additionally, we demonstrate that our method generalizes for use in cities which are not included in the training data.

scholarly journals Automatic Segmentation of Hippocampus and Classification of brain MRI for Alzheimer’s Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1743-1750
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Standard Technique ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Shape Feature

Hippocampus is the structure of brain thatis mostly affected by Alzheimer’s disease at an early stage. Atrophy of hippocampus has been found asa predictive feature for Alzheimer’s disease diagnosis. To measure the atrophy of hippocampus we need to segment it out from surrounding structures of brain. Manual segmentation of hippocampus has beenfound standard technique for hippocampus segmentation in literature, but isvery time consuming and depends on particular anatomical information. In this work we have proposed an automatic approach to segment hippocampus considering texture and active contour from the brain Magnetic Resonance Image. After segmentation, features based on atrophy and shape of hippocampus has beenmeasured. Support vector machine classifier with radial basis function kernel has been analyzed with extracted features for classification of Alzheimer’s and control subjects. In the proposed technique, 200AD MRI and 200control MRI have been considered from Alzheimer’s Disease Neuroimaging Initiative database. The experiment have shown 93% accuracy, 0.96 sensitivity and 0.90specificity with atrophy feature and 94% accuracy, 0.96sensitivity and 0.92specificity with shape feature. Further, 0.96sensitivity, 1 specificity and 98% accuracyhave beenobtained with the fusion of atrophy and shape feature

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