Colm Elliott

Graded classification of fetal heart rate tracings: association with neonatal metabolic acidosis and neurologic morbidity.

OBJECTIVE The objective of the study was to measure the performance of a 5-tier, color-coded graded classification of electronic fetal monitoring (EFM). STUDY DESIGN We used specialized software to analyze and categorize 7416 hours of EFM from term pregnancies. We measured how often and for how long each of the color-coded levels appeared in 3 groups of babies: (A) 60 babies with neonatal encephalopathy (NE) and umbilical artery base deficit (BD) levels were greater than 12 mmol/L; (I) 280 babies without NE but with BD greater than 12 mmol/L; and (N) 2132 babies with normal gases. RESULTS The frequency and duration of EFM abnormalities considered more severe in the classification method were highest in group A and lowest in group N. Detecting an equivalent percentage of cases with adverse outcomes required only minutes spent with marked EFM abnormalities compared with much longer periods with lesser abnormalities. CONCLUSION Both degree and duration of tracing abnormality are related to outcome. We present empirical data quantifying that relationship in a systematic fashion.

Temporally Consistent Probabilistic Detection of New Multiple Sclerosis Lesions in Brain MRI

Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.

White matter changes in paediatric multiple sclerosis and monophasic demyelinating disorders.

See Hacohen et al. (doi:10.1093/awx075) for a scientific commentary on this article. Most children who experience an acquired demyelinating syndrome of the central nervous system will have a monophasic disease course, with no further clinical or radiological symptoms. A subset will be diagnosed with multiple sclerosis, a life-long disorder. Using linear mixed effects models we examined longitudinal diffusion properties of normal-appearing white matter in 505 serial scans of 132 paediatric participants with acquired demyelinating syndromes followed for a median of 4.4 years, many from first clinical presentation, and 106 scans of 80 healthy paediatric participants. Fifty-three participants with demyelinating syndromes eventually received a diagnosis of paediatric-onset multiple sclerosis. Diffusion tensor imaging measures properties of water diffusion through tissue, which normally becomes increasingly restricted and anisotropic in the brain during childhood and adolescence, as fibre bundles develop and myelinate. In the healthy paediatric participants, our data demonstrate the expected trajectory of more restricted and anisotropic white matter diffusivity with increasing age. However, in participants with multiple sclerosis, fractional anisotropy decreased and mean diffusivity of non-lesional, normal-appearing white matter progressively increased after clinical presentation, suggesting not only a failure of age-expected white matter development but also a progressive loss of tissue integrity. Surprisingly, patients with monophasic disease failed to show age-expected changes in diffusion parameters in normal-appearing white matter, although they did not show progressive loss of integrity over time. Further analysis demonstrated that participants with monophasic disease experienced different post-onset trajectories in normal-appearing white matter depending on their presenting phenotype: those with acute disseminated encephalomyelitis demonstrated abnormal trajectories of diffusion parameters compared to healthy paediatric participants, as did patients with non-acute disseminated encephalomyelitis presentations associated with lesions in the brain at onset. Patients with monofocal syndromes such as optic neuritis, transverse myelitis, or isolated brainstem syndromes in whom multifocal brain lesions were absent, showed trajectories more closely approximating normal-appearing white matter development. Our findings also suggest the existence of sexual dimorphism in the effects of demyelinating syndromes on normal-appearing white matter development. Overall, we demonstrate failure of white matter maturational changes and progressive loss of white matter integrity in paediatric-onset multiple sclerosis, but also show that even a single demyelinating attack-when associated with white matter lesions in the brain-negatively impacts subsequent normal-appearing white matter development.

Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images

Accurate and precise identification of multiple sclerosis (MS) lesions in longitudinal MRI is important for monitoring disease progression and for assessing treatment effects. We present a probabilistic framework to automatically detect new, enlarging and resolving lesions in longitudinal scans of MS patients based on multimodal subtraction magnetic resonance (MR) images. Our Bayesian framework overcomes registration artifact by explicitly modeling the variability in the difference images, the tissue transitions, and the neighbourhood classes in the form of likelihoods, and by embedding a classification of a reference scan as a prior. Our method was evaluated on (a) a scan-rescan data set consisting of 3 MS patients and (b) a multicenter clinical data set consisting of 212 scans from 89 RRMS (relapsing-remitting MS) patients. The proposed method is shown to identify MS lesions in longitudinal MRI with a high degree of precision while remaining sensitive to lesion activity.

A Generative Model for Automatic Detection of Resolving Multiple Sclerosis Lesions

The appearance of new Multiple Sclerosis (MS) lesions on MRI is usually followed by subsequent partial resolution, where portions of the newly formed lesion return to isointensity. This resolution is thought to be due mostly to reabsorption of edema, but may also reflect other reparatory processes such as remyelination. Automatic identification of resolving portions of new lesions can provide a marker of repair, allow for automated analysis of MS lesion dynamics, and, when coupled with a method for detection of new MS lesions, provide a tool for precisely measuring lesion change in serial MRI. We present a method for automatic detection of resolving MS lesion voxels in serial MRI using a Bayesian framework that incorporates models for MRI intensities, MRI intensity differences across scans, lesion size, relative position of voxels within a lesion, and time since lesion onset. We couple our method with an existing method for automatic detection of new MS lesions to provide an automated framework for measuring lesion change across serial scans of the same subject. We validate our framework by comparing to lesion volume change measurements derived from expert semi-manual lesion segmentations on clinical trial data consisting of 292 scans from 73 (54 treated, 19 untreated) subjects. Our automated framework shows a) a large improvement in segmentation consistency over time and b) an increased effect size as calculated from measured change in lesion volume for treated and untreated subjects.

Using Deep Learning Algorithms to Automatically Identify the Brain MRI Contrast: Implications for Managing Large Databases

Neuroimaging science has seen a recent explosion in dataset size driving the need to develop database management with efficient processing pipelines. Multi-center neuroimaging databases consistently receive magnetic resonance imaging (MRI) data with unlabeled or incorrectly labeled contrast. There is a need to automatically identify the contrast of MRI scans to save database-managing facilities valuable resources spent by trained technicians required for visual inspection. We developed a deep learning (DL) algorithm with convolution neural network architecture to automatically infer the contrast of MRI scans based on the image intensity of multiple slices. For comparison, we developed a random forest (RF) algorithm to automatically infer the contrast of MRI scans based on acquisition parameters. The DL algorithm was able to automatically identify the MRI contrast of an unseen dataset with <0.2% error rate. The RF algorithm was able to identify the MRI contrast of the same dataset with 1.74% error rate. Our analysis showed that reduced dataset sizes caused the DL algorithm to lose generalizability. Finally, we developed a confidence measure, which made it possible to detect, with 100% specificity, all MRI volumes that were misclassified by the DL algorithm. This confidence measure can be used to alert the user on the need to inspect the small fraction of MRI volumes that are prone to misclassification. Our study introduces a practical solution for automatically identifying the MRI contrast. Furthermore, it demonstrates the powerful combination of convolution neural networks and DL for analyzing large MRI datasets.

Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials.

A variety of automatic segmentation techniques have been successfully applied to the delineation of larger T2 lesions in patient MRI in the context of Multiple Sclerosis (MS), assisting in the estimation of lesion volume, a common clinical measure of disease activity and stage. In the context of clinical trials, however, a wider number of metrics are required to determine the “burden of disease” and activity in order to measure treatment efficacy. These include: (1) the number and volume of T2 lesions in MRI, (2) the number of new and enlarging T2 volumes in longitudinal MRI, and (3) the number of gadolinium enhancing lesions in T1 MRI, the portion of lesions that enhance in T1w MRI after injection with a contrast agent, often associated with active inflammations. In this context, accurate lesion detection must ensure that even small lesions (e.g. 3 to 10 voxels) are detected as they are prevalent in trials. Manual or semi-manual approaches are too time-consuming, inconsistent and expensive to be practical in large clinical trials. To this end, we present a series of fully-automatic, probabilistic machine learning frameworks to detect and segment all lesions in patient MRI, and show their accuracy and robustness in large multi-center, multi-scanner, clinical trial datasets. Several of these algorithms have been placed into a commercial software analysis pipeline, where they have assisted in improving the efficiency and precision of the development of most new MS treatments worldwide. Recent work has shown how a new Bag-of-Lesions brain representation can be used in the context of clinical trials to automatically predict the probability of future disease activity and potential treatment responders, leading to the possibility of personalized medicine.