An anomaly detection approach to identify chronic brain infarcts on MRI

Hespen, Kees M. van; Zwanenburg, Jaco J.M.; Dankbaar, Jan Willem; Geerlings, Mirjam I.; Hendrikse, Jeroen; Kuijf, Hugo J.

doi:10.1038/s41598-021-87013-4

Cited by 41 publications

(26 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most studies using these datasets generally focus on the average imaging characteristics at a group level. There has been much less work on studying outlying individuals and the associated imaging phenotypes in these large neuroimaging datasets (Marquand et al, 2016; Mourao‐Miranda et al, 2011; Pinaya et al, 2019; van Hespen et al, 2021). To begin to fill this gap, we set out to investigate individual outliers from more than 15,000 UKB subjects.…”

Section: Discussionmentioning

confidence: 99%

“…One common way is based on whether the method makes use of labeled datasets to train the outlier detection model: supervised methods use labeled datasets that contain both labeled outliers and labeled non‐outliers for training; semi‐supervised methods use labeled datasets that only contain labeled non‐outliers for training; and unsupervised methods use unlabeled datasets for training (Goldstein & Uchida, 2016). Using the available diagnostic labels for all subjects or at least the non‐outlier subjects, outlier detection studies have employed a variety of algorithms, such as one‐class support vector machine, Gaussian process regression, or autoencoders, and these have been applied in a supervised or semi‐supervised manner to quantify the outlierness of healthy individuals or patients (Marquand, Rezek, Buitelaar, & Beckmann, 2016; Mourao‐Miranda et al, 2011; Pinaya, Mechelli, & Sato, 2019; van Hespen et al, 2021). However, diagnostic labels are not always available, making the supervised or semi‐supervised approaches challenging to implement across the board.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains

Reich

Dembling

et al. 2021

Human Brain Mapping

View full text Add to dashboard Cite

Outliers in neuroimaging represent spurious data or the data of unusual phenotypes that deserve special attention such as clinical follow‐up. Outliers have usually been detected in a supervised or semi‐supervised manner for labeled neuroimaging cohorts. There has been much less work using unsupervised outlier detection on large unlabeled cohorts like the UK Biobank brain imaging dataset. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes. Here, we developed a two‐level outlier detection and screening methodology to characterize individual outliers from the multimodal MRI dataset of more than 15,000 UK Biobank subjects. In primary screening, using brain ventricles, white matter, cortical thickness, and functional connectivity‐based imaging phenotypes, every subject was parameterized with an outlier score per imaging phenotype. Outlier scores of these imaging phenotypes had good‐to‐excellent test–retest reliability, with the exception of resting‐state functional connectivity (RSFC). Due to the low reliability of RSFC outlier scores, RSFC outliers were excluded from further individual‐level outlier screening. In secondary screening, the extreme outliers (1,026 subjects) were examined individually, and those arising from data collection/processing errors were eliminated. A representative subgroup of 120 subjects from the remaining non‐artifactual outliers were radiologically reviewed, and radiological findings were identified in 97.5% of them. This study establishes an unsupervised framework for investigating rare individual imaging phenotypes within a large neuroimaging cohort.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains

Reich

Dembling

et al. 2021

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Most studies using these datasets generally focus on the average imaging characteristics at a group level. There has been much less work on studying outlying individuals and the associated imaging phenotypes in these large neuroimaging datasets (Marquand et al, 2016;Mourao-Miranda et al, 2011;Pinaya et al, 2019;van Hespen et al, 2021). To begin to fill this gap, we set out to investigate individual outliers from about 20,000 UKB subjects.…”

Section: The Approach To Screen Individual Outliers In a Large Neuroimaging Datasetmentioning

confidence: 99%

“…One common way is based on whether the method makes use of labeled datasets to train the outlier detection model: supervised methods use labeled datasets that contain both labeled outliers and labeled non-outliers for training; semi-supervised methods use labeled datasets that only contain labeled non-outliers for training; and unsupervised methods use unlabeled datasets for training (Goldstein & Uchida, 2016). Using the available diagnostic labels for all subjects or at least the non-outlier subjects, outlier detection studies have employed a variety of algorithms, such as one-class support vector machine, Gaussian process regression, or autoencoders, and these have been applied in a supervised or semi-supervised manner to quantify the outlierness of healthy individuals or patients (Marquand, Rezek, Buitelaar, & Beckmann, 2016;Mourao-Miranda et al, 2011;Pinaya, Mechelli, & Sato, 2019;van Hespen et al, 2021). However, diagnostic labels are not always available, making the supervised or semi-supervised approaches challenging to implement across the board.…”

Section: Introductionmentioning

confidence: 99%

Outlier detection in multimodal MRI identifies rare individual phenotypes among 20,000 brains

Reich

Dembling

et al. 2021

Preprint

View full text Add to dashboard Cite

The UK Biobank (UKB) is a large-scale epidemiological study and its imaging component focuses on the pre-symptomatic participants. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes and mechanisms. Identifying these rare phenotypes is often referred to as "anomaly detection", or "outlier detection". However, anomaly detection in neuroimaging has usually been applied in a supervised or semi-supervised manner for clinically defined cohorts of relatively small size. There has been much less work using anomaly detection on large unlabeled cohorts like the UKB. Here we developed a two-level anomaly screening methodology to systematically identify anomalies from ~19,000 UKB subjects. The same method was also applied to ~1,000 young healthy subjects from the Human Connectome Project (HCP). In primary screening, using ventricular, white matter, and gray matter-based imaging phenotypes derived from multimodal MRI, every subject was parameterized with an anomaly score per phenotype to quantitate the degree of abnormality. These anomaly scores were highly robust. Anomaly score distributions of the UKB cohort were all more outlier-prone than the HCP cohort of young adults. The approach enabled the assessments of test-retest reliability via the anomaly scores, which ranged from excellent reliability for ventricular volume, white matter lesion volume, and fractional anisotropy, to good reliability for mean diffusivity and cortical thickness. In secondary screening, the anomalies due to data collection/processing errors were eliminated. A subgroup of the remaining anomalies were radiologically reviewed, and a substantial percentage of them (UKB: 90.1%; HCP: 42.9%) had various brain pathologies such as masses, cysts, white matter lesions, infarcts, encephalomalacia, or prominent sulci. The remaining anomalies of the subgroup had unexplained causes and would be interesting for follow-up. Finally, we show that anomaly detection applied to resting-state functional connectivity did not identify any reliable anomalies, which was attributed to the confounding effects of brain-wide signal variation. Together, this study establishes an unsupervised framework for investigating rare individual imaging phenotypes within large heterogeneous cohorts.

show abstract

“…Inspired by recent works on disentangled representations [26][27][28][29][30][31] , and the promising findings of anomaly detection in Brain MRI [32][33][34][35][36][37] , we propose a federated, unsupervised, domain-agnostic federated method to segment multiple abnormal brain MR findings (multiple sclerosis and tumors). Fig-…”

Section: Introductionmentioning

confidence: 99%

Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection

Bercea

Wiestler²,

Rueckert

et al. 2021

Preprint

View full text Add to dashboard Cite

Recent advances in Deep Learning (DL) and the increased use of brain MRI have provided a great opportunity and interest in automated anomaly segmentation to support human interpretation and improve clinical workflow. However, medical imaging must be curated by trained clinicians, which is time-consuming and expensive. Further, data is often scattered across multiple institutions, with privacy regulations limiting its access. Here, we present FedDis (Federated Disentangled representation learning for unsupervised brain pathology segmentation) to collaboratively train an unsupervised deep convolutional neural network on 1532 healthy MR scans from four different institutions, and evaluate its performance in identifying abnormal brain MRIs including multiple sclerosis (MS) lesions, low-grade tumors (LGG), and high-grade tumors/glioblastoma (HGG/GB) on a total of ~500 scans from 5 different institutions and datasets. FedDis mitigates the statistical heterogeneity given by different scanners by disentangling the parameter space into global, i.e., shape and local, i.e., appearance. We only share the former with the federated clients to leverage common anatomical structure while keeping client-specific contrast information private. We have shown that our collaborative approach, FedDis, improves anomaly segmentation results by 99.74% for MS and 40.45% for tumors over locally trained models without the need for annotations or sharing private local data. We found out that FedDis is especially beneficial for clients that share both healthy and anomaly data coming from the same institute, improving their local anomaly detection performance by up to 227% for MS lesions and 77% for brain tumors.

show abstract

An anomaly detection approach to identify chronic brain infarcts on MRI

Cited by 41 publications

References 55 publications

Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains

Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains

Outlier detection in multimodal MRI identifies rare individual phenotypes among 20,000 brains

Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection

Contact Info

Product

Resources

About