Bridging generative models and Convolutional Neural Networks for domain-agnostic segmentation of brain MRI

Segmentation of brain MRI scans is paramount in neuroimaging, as it is a prerequisite for many subsequent analyses. Although manual segmentation is considered the gold standard, it suffers from severe reproducibility issues, and is extremely tedious, which limits its application to large datasets. Therefore, there is a clear need for automated tools that enable fast and accurate segmentation of brain MRI scans. Recent methods rely on convolutional neural networks (CNNs). While CNNs obtain accurate results on their training domain, they are highly sensitive to changes in resolution and MRI contrast. Although data augmentation and domain adaptation techniques can increase the generalisability of CNNs, these methods still need to be retrained for every new domain, which requires costly labelling of images. Here, we present a learning strategy to make CNNs agnostic to MRI contrast, resolution, and numerous artefacts. Specifically, we train a network with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation approach where all generation parameters are drawn for each example from uniform priors. As a result, the network is forced to learn domain-agnostic features, and can segment real test scans without retraining. The proposed method almost achieves the accuracy of supervised CNNs on their training domain, and substantially outperforms state-of-the-art domain adaptation methods. Finally, based on this learning strategy, we present a segmentation suite for robust analysis of heterogeneous clinical scans. Overall, our approach unlocks the development of morphometry on millions of clinical scans, which ultimately has the potential to improve the diagnosis and characterisation of neurological disorders

Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyse such scans could transform neuroimaging research. Yet, their potential remains untapped, since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artefacts, subject populations). Here we present SynthSeg+, an AI segmentation suite that enables, for the first time, robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg+ is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry.Comment: under review, extension of MICCAI 2022 pape

AnyStar: Domain randomized universal star-convex 3D instance segmentation

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM, and placental cotyledons in human fetal MRI, all without any retraining, finetuning, transfer learning, or domain adaptation. Code is available at https://github.com/neel-dey/AnyStar.Comment: Code available at https://github.com/neel-dey/AnySta

A cluster of outflows in the Vulpecula Rift

We present $^{12}$CO, $^{13}$CO and C$^{18}$O (J=3$-$2) observations of a new cluster of outflows in the Vulpecula Rift with HARP-B on the JCMT. The mass associated with the outflows, measured using the $^{12}$CO HARP-B observations and assuming a distance to the region of 2.3 kpc, is 129 \msol{}, while the mass associated with the dense gas from C$^{18}$O observations is 458 \msol{} and the associated sub-millimeter core has a mass of 327 $\pm$ 112 \msol{} independently determined from Bolocam 1.1mm data. The outflow-to-core mass ratio is therefore $\sim$0.4, making this region one of the most efficient observed thus far with more than an order of magnitude more mass in the outflow than would be expected based on previous results. The kinetic energy associated with the flows, 94$\times10^{45}$ ergs, is enough to drive the turbulence in the local clump, and potentially unbind the local region altogether. The detection of SiO (J=8$-$7) emission toward the outflows indicates that the flow is still active, and not simply a fossil flow. We also model the SEDs of the four YSOs associated with the molecular material, finding them all to be of mid to early B spectral type. The energetic nature of the outflows and significant reservoir of cold dust detected in the sub-mm suggest that these intermediate mass YSOs will continue to accrete and become massive, rather than reach the main sequence at their current mass.Comment: 11 pages, 8 figures and 3 tables. Accepted to MNRAS. A higher-resolution version of figure 1 will be included in the published version and is available from the authors upon request. Updated with red and blue wings swapped to match doppler shif

In vivo hypothalamic regional volumetry across the frontotemporal dementia spectrum

BACKGROUND: Frontotemporal dementia (FTD) is a spectrum of diseases characterised by language, behavioural and motor symptoms. Among the different subcortical regions implicated in the FTD symptomatology, the hypothalamus regulates various bodily functions, including eating behaviours which are commonly present across the FTD spectrum. The pattern of specific hypothalamic involvement across the clinical, pathological, and genetic forms of FTD has yet to be fully investigated, and its possible associations with abnormal eating behaviours have yet to be fully explored. METHODS: Using an automated segmentation tool for volumetric T1-weighted MR images, we measured hypothalamic regional volumes in a cohort of 439 patients with FTD (197 behavioural variant FTD [bvFTD]; 7 FTD with associated motor neurone disease [FTD-MND]; 99 semantic variant primary progressive aphasia [svPPA]; 117 non-fluent variant PPA [nfvPPA]; 19 PPA not otherwise specified [PPA-NOS]) and 118 age-matched controls. We compared volumes across the clinical, genetic (29 MAPT, 32 C9orf72, 23 GRN), and pathological diagnoses (61 tauopathy, 40 TDP-43opathy, 4 FUSopathy). We correlated the volumes with presence of abnormal eating behaviours assessed with the revised version of the Cambridge Behavioural Inventory (CBI-R). RESULTS: On average, FTD patients showed 14% smaller hypothalamic volumes than controls. The groups with the smallest hypothalamic regions were FTD-MND (20%), MAPT (25%) and FUS (33%), with differences mainly localised in the anterior and posterior regions. The inferior tuberal region was only significantly smaller in tauopathies (MAPT and Pick’s disease) and in TDP-43 type C compared to controls and was the only regions that did not correlate with eating symptoms. PPA-NOS and nfvPPA were the groups with the least frequent eating behaviours and the least hypothalamic involvement. CONCLUSIONS: Abnormal hypothalamic volumes are present in all the FTD forms, but different hypothalamic regions might play a different role in the development of abnormal eating behavioural and metabolic symptoms. These findings might therefore help in the identification of different underlying pathological mechanisms, suggesting the potential use of hypothalamic imaging biomarkers and the research of potential therapeutic targets within the hypothalamic neuropeptides

Revealing evolved massive stars with Spitzer

Massive evolved stars loss a large fraction of their mass via copious stellar wind or instant outbursts and during certain evolutionary phases they can be identified via the presence of their circumstellar nebulae. In this paper, we present the results of search for compact nebulae (reminiscent of circumstellar nebulae around evolved massive stars) using archival 24 $\mu$m data obtained with the Multiband Imaging Photometer for Spitzer. We discovered 115 nebulae, most of which bear a striking resemblance to the circumstellar nebulae associated with Luminous Blue Variables (LBVs) and late WN-type (WNL) Wolf-Rayet (WR) stars in the Milky Way and the Large Magellanic Cloud (LMC). We interpret this similarity as an indication that the central stars of detected nebulae are either LBVs or related evolved massive stars. Our interpretation is supported by follow-up spectroscopy of two dozens of these central stars, most of which turns out to be either candidate LBVs (cLBVs), blue supergiants or WNL stars. We expect that the forthcoming spectroscopy of the remaining objects from our list, accompanied by the spectrophotometric monitoring of the already discovered cLBVs, will further increase the known population of Galactic LBVs, which in turn would have profound consequences for better understanding the LBV phenomenon and its role in the transition between hydrogen burning O stars and helium burning WR stars. We also report the detection of an arc-like structure attached to the cLBV HD326823 and an arc associated with the LBV R99 (HD269445) in the LMC.Comment: 9 pages, 10 figures, accepted to MNRA

Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH ($\rho$=.85) and hippocampal volumes (r=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg