Zebrafish vascular quantification: a tool for quantification of three-dimensional zebrafish cerebrovascular architecture by automated image analysis.

Zebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into three-dimensional vascular development in vivo. However, quantification of the zebrafish cerebral vasculature in 3D remains highly challenging. Here, we describe and test an image analysis workflow for 3D quantification of the total or regional zebrafish brain vasculature, called zebrafish vasculature quantification (ZVQ). It provides the first landmark- or object-based vascular inter-sample registration of the zebrafish cerebral vasculature, producing population average maps allowing rapid assessment of intra- and inter-group vascular anatomy. ZVQ also extracts a range of quantitative vascular parameters from a user-specified region of interest, including volume, surface area, density, branching points, length, radius and complexity. Application of ZVQ to 13 experimental conditions, including embryonic development, pharmacological manipulations and morpholino-induced gene knockdown, shows that ZVQ is robust, allows extraction of biologically relevant information and quantification of vascular alteration, and can provide novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface and workflow documentation are provided. Together, ZVQ provides the first open-source quantitative approach to assess the 3D cerebrovascular architecture in zebrafish.

A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage.

Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.

Cerebrovascular endothelial cells form transient Notch-dependent cystic structures in zebrafish.

We identify a novel endothelial membrane behaviour in transgenic zebrafish. Cerebral blood vessels extrude large transient spherical structures that persist for an average of 23 min before regressing into the parent vessel. We term these structures "kugeln", after the German for sphere. Kugeln are only observed arising from the cerebral vessels and are present as late as 28 days post fertilization. Kugeln do not communicate with the vessel lumen and can form in the absence of blood flow. They contain little or no cytoplasm, but the majority are highly positive for nitric oxide reactivity. Kugeln do not interact with brain lymphatic endothelial cells (BLECs) and can form in their absence, nor do they perform a scavenging role or interact with macrophages. Inhibition of actin polymerization, Myosin II, or Notch signalling reduces kugel formation, while inhibition of VEGF or Wnt dysregulation (either inhibition or activation) increases kugel formation. Kugeln represent a novel Notch-dependent NO-containing endothelial organelle restricted to the cerebral vessels, of currently unknown function.

Patterns of Ancestral Animal Codon Usage Bias Revealed through Holozoan Protists.

Choanoflagellates and filastereans are the closest known single celled relatives of Metazoa within Holozoa and provide insight into how animals evolved from their unicellular ancestors. Codon usage bias has been extensively studied in metazoans, with both natural selection and mutation pressure playing important roles in different species. The disparate nature of metazoan codon usage patterns prevents the reconstruction of ancestral traits. However, traits conserved across holozoan protists highlight characteristics in the unicellular ancestors of Metazoa. Presented here are the patterns of codon usage in the choanoflagellates Monosiga brevicollis and Salpingoeca rosetta, as well as the filasterean Capsaspora owczarzaki. Codon usage is shown to be remarkably conserved. Highly biased genes preferentially use GC-ending codons, however there is limited evidence this is driven by local mutation pressure. The analyses presented provide strong evidence that natural selection, for both translational accuracy and efficiency, dominates codon usage bias in holozoan protists. In particular, the signature of selection for translational accuracy can be detected even in the most weakly biased genes. Biased codon usage is shown to have coevolved with the tRNA species, with optimal codons showing complementary binding to the highest copy number tRNA genes. Furthermore, tRNA modification is shown to be a common feature for amino acids with higher levels of degeneracy and highly biased genes show a strong preference for using modified tRNAs in translation. The translationally optimal codons defined here will be of benefit to future transgenics work in holozoan protists, as their use should maximise protein yields from edited transgenes.

Neurologic Deficits in Patients With Newly Diagnosed Celiac Disease Are Frequent and Linked With Autoimmunity to Transglutaminase 6.

BACKGROUND & AIMS: Celiac disease is an autoimmune disorder induced by ingestion of gluten that affects 1% of the population and is characterized by gastrointestinal symptoms, weight loss, and anemia. We evaluated the presence of neurologic deficits and investigated whether the presence of antibodies to Transglutaminase 6 (TG6) increases the risk of neurologic defects in patients with a new diagnosis of celiac disease. METHODS: We performed a prospective cohort study at a secondary-care gastroenterology center of 100 consecutive patients who received a new diagnosis of celiac disease based on gastroscopy and duodenal biopsy. We collected data on neurologic history, and patients were evaluated in a clinical examination along with magnetic resonance imaging of the brain, magnetic resonance (MR) spectroscopy of the cerebellum, and measurements of antibodies against TG6 in serum samples. The first 52 patients recruited underwent repeat MR spectroscopy at 1 year after a gluten-free diet (GFD). The primary aim was to establish if detection of antibodies against TG6 can be used to identify patients with celiac disease and neurologic dysfunction. RESULTS: Gait instability was reported in 24% of the patients, persisting sensory symptoms in 12%, and frequent headaches in 42%. Gait ataxia was found in 29% of patients, nystagmus in 11%, and distal sensory loss in 10%. Sixty percent of patients had abnormal results from magnetic resonance imaging, 47% had abnormal results from MR spectroscopy of the cerebellum, and 25% had brain white matter lesions beyond that expected for their age group. Antibodies against TG6 were detected in serum samples from 40% of patients-these patients had significant atrophy of subcortical brain regions compared with patients without TG6 autoantibodies. In patients with abnormal results from MR spectroscopy of the cerebellum, those on the GFD had improvements detected in the repeat MR spectroscopy 1 year later. CONCLUSIONS: In a prospective cohort study of patients with a new diagnosis of celiac disease at a gastroenterology clinic, neurologic deficits were common and 40% had circulating antibodies against TG6. We observed a significant reduction in volume of specific brain regions in patients with TG6 autoantibodies, providing evidence for a link between autoimmunity to TG6 and brain atrophy in patients with celiac disease. There is a need for early diagnosis, increased awareness of the neurologic manifestations among clinicians, and reinforcement of adherence to a strict GFD by patients to avoid permanent neurologic disability.

Do Optic Canal Dimensions Measured on CT Influence the Degree of Papilloedema and Visual Dysfunction in Idiopathic Intracranial Hypertension?

A recent study found that increased optic canal area on magnetic resonance imaging was associated with worse papilloedema in idiopathic intracranial hypertension (IIH). We repeated this study using more accurate computerized tomography derived measurements. Optic canal dimensions were measured from 42 IIH patients and 24 controls.  These were compared with papilloedema grade.  There was no correlation between any of the optic canal measurements and papilloedema grade and no significant difference in optic canal measurements between patients and controls. Our results cast doubt on the existing literature regarding the association between optic canal size and the degree of papilloedema in IIH. CT delineates bony anatomy more accurately than MRI and our CT-derived optic canal measurements cast doubt on the existing literature regarding the association between optic canal size and the degree of Papilloedema in IIH.

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance.

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform's coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58-0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53-0.72)) and comparable between both the observers (κ = 0.68 (0.61-0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

Metric to quantify white matter damage on brain magnetic resonance images.

PURPOSE: Quantitative assessment of white matter hyperintensities (WMH) on structural Magnetic Resonance Imaging (MRI) is challenging. It is important to harmonise results from different software tools considering not only the volume but also the signal intensity. Here we propose and evaluate a metric of white matter (WM) damage that addresses this need. METHODS: We obtained WMH and normal-appearing white matter (NAWM) volumes from brain structural MRI from community dwelling older individuals and stroke patients enrolled in three different studies, using two automatic methods followed by manual editing by two to four observers blind to each other. We calculated the average intensity values on brain structural fluid-attenuation inversion recovery (FLAIR) MRI for the NAWM and WMH. The white matter damage metric is calculated as the proportion of WMH in brain tissue weighted by the relative image contrast of the WMH-to-NAWM. The new metric was evaluated using tissue microstructure parameters and visual ratings of small vessel disease burden and WMH: Fazekas score for WMH burden and Prins scale for WMH change. RESULTS: The correlation between the WM damage metric and the visual rating scores (Spearman ρ > =0.74, p < 0.0001) was slightly stronger than between the latter and WMH volumes (Spearman ρ > =0.72, p < 0.0001). The repeatability of the WM damage metric was better than WM volume (average median difference between measurements 3.26% (IQR 2.76%) and 5.88% (IQR 5.32%) respectively). The follow-up WM damage was highly related to total Prins score even when adjusted for baseline WM damage (ANCOVA, p < 0.0001), which was not always the case for WMH volume, as total Prins was highly associated with the change in the intense WMH volume (p = 0.0079, increase of 4.42 ml per unit change in total Prins, 95%CI [1.17 7.67]), but not with the change in less-intense, subtle WMH, which determined the volumetric change. CONCLUSION: The new metric is practical and simple to calculate. It is robust to variations in image processing methods and scanning protocols, and sensitive to subtle and severe white matter damage.

Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability.

There is evidence that subtle breakdown of the blood-brain barrier (BBB) is a pathophysiological component of several diseases, including cerebral small vessel disease and some dementias. Dynamic contrast-enhanced MRI (DCE-MRI) combined with tracer kinetic modelling is widely used for assessing permeability and perfusion in brain tumours and body tissues where contrast agents readily accumulate in the extracellular space. However, in diseases where leakage is subtle, the optimal approach for measuring BBB integrity is likely to differ since the magnitude and rate of enhancement caused by leakage are extremely low; several methods have been reported in the literature, yielding a wide range of parameters even in healthy subjects. We hypothesised that the Patlak model is a suitable approach for measuring low-level BBB permeability with low temporal resolution and high spatial resolution and brain coverage, and that normal levels of scanner instability would influence permeability measurements. DCE-MRI was performed in a cohort of mild stroke patients (n=201) with a range of cerebral small vessel disease severity. We fitted these data to a set of nested tracer kinetic models, ranking their performance according to the Akaike information criterion. To assess the influence of scanner drift, we scanned 15 healthy volunteers that underwent a "sham" DCE-MRI procedure without administration of contrast agent. Numerical simulations were performed to investigate model validity and the effect of scanner drift. The Patlak model was found to be most appropriate for fitting low-permeability data, and the simulations showed vp and K(Trans) estimates to be reasonably robust to the model assumptions. However, signal drift (measured at approximately 0.1% per minute and comparable to literature reports in other settings) led to systematic errors in calculated tracer kinetic parameters, particularly at low permeabilities. Our findings justify the growing use of the Patlak model in low-permeability states, which has the potential to provide valuable information regarding BBB integrity in a range of diseases. However, absolute values of the resulting tracer kinetic parameters should be interpreted with extreme caution, and the size and influence of signal drift should be measured where possible.

On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology.

INTRODUCTION: Subtle inhomogeneities in the scanner's magnetic fields (B0 and B1) alter the intensity levels of the structural magnetic resonance imaging (MRI) affecting the volumetric assessment of WMH changes. Here, we investigate the influence that (1) correcting the images for the B1 inhomogeneities (i.e. bias field correction (BFC)) and (2) selection of the WMH change assessment method can have on longitudinal analyses of WMH progression and discuss possible solutions. METHODS: We used brain structural MRI from 46 mild stroke patients scanned at stroke onset and 3 years later. We tested three BFC approaches: FSL-FAST, N4 and exponentially entropy-driven homomorphic unsharp masking (E(2)D-HUM) and analysed their effect on the measured WMH change. Separately, we tested two methods to assess WMH changes: measuring WMH volumes independently at both time points semi-automatically (MCMxxxVI) and subtracting intensity-normalised FLAIR images at both time points following image gamma correction. We then combined the BFC with the computational method that performed best across the whole sample to assess WMH changes. RESULTS: Analysis of the difference in the variance-to-mean intensity ratio in normal tissue between BFC and uncorrected images and visual inspection showed that all BFC methods altered the WMH appearance and distribution, but FSL-FAST in general performed more consistently across the sample and MRI modalities. The WMH volume change over 3 years obtained with MCMxxxVI with vs. without FSL-FAST BFC did not significantly differ (medians(IQR)(with BFC) = 3.2(6.3) vs. 2.9(7.4)ml (without BFC), p = 0.5), but both differed significantly from the WMH volume change obtained from subtracting post-processed FLAIR images (without BFC)(7.6(8.2)ml, p < 0.001). This latter method considerably inflated the WMH volume change as subtle WMH at baseline that became more intense at follow-up were counted as increase in the volumetric change. CONCLUSIONS: Measurement of WMH volume change remains challenging. Although the overall volumetric change was not significantly affected by the application of BFC, these methods distorted the image intensity distribution affecting subtle WMH. Subtracting the FLAIR images at both time points following gamma correction seems a promising technique but is adversely affected by subtle WMH. It is important to take into account not only the changes in volume but also in the signal intensity.

Quantification of total fetal brain volume using 3D MR imaging data acquired in utero.

OBJECTIVE: Interpretation of magnetic resonance (MR) imaging of the fetal brain in utero is primarily undertaken using 2D images to provide anatomical information about structural abnormalities. It is now possible to obtain 3D image acquisitions that allow measurement of fetal brain volumes that are potentially useful clinically. The aim of our current work is to provide reference values of total brain volumes obtained from a cohort of low risk fetuses with no abnormalities on ante-natal ultrasonography and in utero MR imaging. METHOD: Images from volume MR acquisitions of 132 fetuses were used to extract brain volumes by manual segmentation. Reproducibility and reliability were assessed by analysis of the results of two subgroups who had repeated measurements made by the primary and a secondary observer. RESULTS: Intra-observer and inter-observer agreement was high with no statistically significant differences between and within observers (p = 0.476 and p = 0.427, respectively). The results of the brain volume assessments are presented graphically with mean and 95% prediction limits alongside estimates of normal growth rates. CONCLUSION: We have shown that fetal brain volumes can be reliably extracted from in utero MR (iuMR) imaging 3D datasets with a high degree of reproducibility. The resultant data could potentially be used as a reference tool in the clinical setting. © 2016 John Wiley & Sons, Ltd.

Proton spectroscopic imaging of brain metabolites in basal ganglia of healthy older adults.

OBJECT: We sought to measure brain metabolite levels in healthy older people. MATERIALS AND METHODS: Spectroscopic imaging at the level of the basal ganglia was applied in 40 participants aged 73-74 years. Levels of the metabolites N-acetyl aspartate (NAA), choline, and creatine were determined in "institutional units" (IU) corrected for T1 and T2 relaxation effects. Structural imaging enabled determination of grey matter (GM), white matter (WM), and cerebrospinal fluid content. ANOVA analysis was carried out for voxels satisfying quality criteria. RESULTS: Creatine levels were greater in GM than WM (57 vs. 44 IU, p < 0.001), whereas choline and NAA levels were greater in WM than GM [13 vs. 10 IU (p < 0.001) and 76 versus 70 IU (p = 0.03), respectively]. The ratio of NAA/cre was greater in WM than GM (2.1 vs. 1.4, p = 0.001) as was that of cho/cre (0.32 vs. 0.16, p < 0.001). A low voxel yield was due to brain atrophy and the difficulties of shimming over an extended region of brain. CONCLUSION: This study addresses the current lack of information on brain metabolite levels in older adults. The normal features of ageing result in a substantial loss of reliable voxels and should be taken into account when planning studies. Improvements in shimming are also required before the methods can be applied more widely.

Diffuse intrinsic pontine glioma: is MRI surveillance improved by region of interest volumetry?

BACKGROUND: Paediatric diffuse intrinsic pontine glioma (DIPG) is noteworthy for its fibrillary infiltration through neuroparenchyma and its resultant irregular shape. Conventional volumetry methods aim to approximate such irregular tumours to a regular ellipse, which could be less accurate when assessing treatment response on surveillance MRI. Region-of-interest (ROI) volumetry methods, using manually traced tumour profiles on contiguous imaging slices and subsequent computer-aided calculations, may prove more reliable. OBJECTIVE: To evaluate whether the reliability of MRI surveillance of DIPGs can be improved by the use of ROI-based volumetry. MATERIALS AND METHODS: We investigated the use of ROI- and ellipsoid-based methods of volumetry for paediatric DIPGs in a retrospective review of 22 MRI examinations. We assessed the inter- and intraobserver variability of the two methods when performed by four observers. RESULTS: ROI- and ellipsoid-based methods strongly correlated for all four observers. The ROI-based volumes showed slightly better agreement both between and within observers than the ellipsoid-based volumes (inter-[intra-]observer agreement 89.8% [92.3%] and 83.1% [88.2%], respectively). Bland-Altman plots show tighter limits of agreement for the ROI-based method. CONCLUSION: Both methods are reproducible and transferrable among observers. ROI-based volumetry appears to perform better with greater intra- and interobserver agreement for complex-shaped DIPG.

Blood-brain barrier permeability and long-term clinical and imaging outcomes in cerebral small vessel disease.

BACKGROUND AND PURPOSE: Increased blood-brain barrier (BBB) permeability occurs in cerebral small vessel disease. It is not known if BBB changes predate progression of small vessel disease. METHODS: We followed-up patients with nondisabling lacunar or cortical stroke and BBB permeability magnetic resonance imaging after their original stroke. Approximately 3 years later, we assessed functional outcome (Oxford Handicap Score, poor outcome defined as 3-6), recurrent neurological events, and white matter hyperintensity (WMH) progression on magnetic resonance imaging. RESULTS: Among 70 patients with mean age of 68 (SD ± 11) years, median time to clinical follow-up was 39 months (interquartile range, 30-45) and median Oxford Handicap Score was 2 (interquartile range, 1-3); poor functional outcome was associated with higher baseline WMH score (P<0.001) and increased basal ganglia BBB permeability (P=0.046). Among 48 patients with follow-up magnetic resonance imaging, WMH progression at follow-up was associated with baseline WMH (ANCOVA P<0.0001) and age (ANCOVA P=0.032). CONCLUSIONS: Further long-term studies to evaluate the role of BBB dysfunction in progression of small vessel disease are required in studies that are large enough to account for key prognostic influences such as baseline WMH and age.

Apparent diffusion coefficient thresholds and diffusion lesion volume in acute stroke.

BACKGROUND: Apparent diffusion coefficient (ADC) thresholds are used to determine acute stroke lesion volume, but the reliability of this approach and comparability to the volume of the magnetic resonance diffusion-weighted imaging (MR-DWI) hyperintense lesion is unclear. METHODS: We prospectively recruited and clinically assessed patients who had experienced acute ischemic stroke and performed DWI less than 24 hours and at 3 to 7 days after stroke. We compared the volume of the manually outlined DW hyperintense lesion (reference standard) with lesion volumes derived from 3 commonly used ADC thresholds: .55 × 10(-3)/mm(2)/second(-1), .65 × 10(-3)/mm(2)/second(-1), and .75 × 10(-3)/mm(2)/second(-1), with and without "editing" of erroneous tissue. We compared the volumes obtained by reference standard, "raw," and "edited" thresholds. RESULTS: Among 33 representative patients, the acute DWI lesion volume was 15,284 mm(3); the median unedited/edited ADC volumes were 52,972/2786 mm(3), 92,707/6,987 mm(3), and 227,681/unmeasureable mm(3) (.55 × 10(-3)/mm(2)/second(-1), .65 × 10(-3)/mm(2)/second(-1), and .75 × 10(-3)/mm(2)/second(-1) thresholds, respectively). Subacute lesions gave similar differences. These differences between edited and unedited diffusion-weighted imaging and ADC volumes were statistically significant. CONCLUSIONS: Threshold-derived ADC volumes require substantial manual editing to avoid over- or underestimating the visible DWI lesion and should be used with caution.

Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study.

Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (pDPN). This supports the idea of using neuroimaging as a mechanism-based technique to individualise therapy for patients with painful DPN. The aim of this study was to use deep learning to predict treatment response in patients with pDPN using resting state functional imaging (rs-fMRI). We divided 43 painful pDPN patients into responders and non-responders to lidocaine treatment (responders n = 29 and non-responders n = 14). We used rs-fMRI to extract functional connectivity features, using group independent component analysis (gICA), and performed automated treatment response deep learning classification with three-dimensional convolutional neural networks (3D-CNN). Using gICA we achieved an area under the receiver operating characteristic curve (AUC) of 96.60% and F1-Score of 95% in a ten-fold cross validation (CV) experiment using our described 3D-CNN algorithm. To our knowledge, this is the first study utilising deep learning methods to classify treatment response in pDPN.

A grid overlay framework for analysis of medical images and its application to the measurement of stroke lesions.

OBJECTIVES: To create and evaluate an interactive software tool for measuring imaging data in situations where hand-drawn region-of-interest measurements are unfeasible, for example, when the structure of interest is patchy with ill-defined boundaries. METHODS: An interactive grid overlay software tool was implemented that enabled coding of voxels dependent on their imaging appearance with a series of user-defined classes. The Grid Analysis Tool (GAT) was designed to automatically extract quantitative imaging data, grouping the results by tissue class. Inter- and intra-observer reproducibility was evaluated by six observers of various backgrounds in a study of acute stroke patients. RESULTS: The software tool enabled a more detailed classification of the stroke lesion than would be possible with a region-of-interest approach. However, inter-observer coefficients of variation (CVs) were relatively high, reaching 70% in "possibly abnormal" tissue and around 15-20% in normal appearing tissues, while intra-observer CVs were no more than 13% in "possibly abnormal" tissue and generally less than 1% in normal-appearing tissues. CONCLUSIONS: The grid-overlay method overcomes some of the limitations of conventional Region Of Interest (ROI) approaches, providing a viable alternative for segmenting patchy lesions with ill-defined boundaries, but care is required to ensure acceptable reproducibility if the method is applied by multiple observers. KEY POINTS: Computer software developed to overcome limitations of conventional regions of interest measurements • This software is suitable for patchy lesions with ill-defined borders • Allows a more detailed assessment of imaging data.

Analytical Approaches for the Segmentation of the Zebrafish Brain Vasculature.

With advancements in imaging techniques, data visualization allows new insights into fundamental biological processes of development and disease. However, although biomedical science is heavily reliant on imaging data, interpretation of datasets is still often based on subjective visual assessment rather than rigorous quantitation. This overview presents steps to validate image processing and segmentation using the zebrafish brain vasculature data acquired with light sheet fluorescence microscopy as a use case. Blood vessels are of particular interest to both medical and biomedical science. Specific image enhancement filters have been developed that enhance blood vessels in imaging data prior to segmentation. Using the Sato enhancement filter as an example, we discuss how filter application can be evaluated and optimized. Approaches from the medical field such as simulated, experimental, and augmented datasets can be used to gain the most out of the data at hand. Using such datasets, we provide an overview of how biologists and data analysts can assess the accuracy, sensitivity, and robustness of their segmentation approaches that allow extraction of objects from images. Importantly, even after optimization and testing of a segmentation workflow (e.g., from a particular reporter line to another or between immunostaining processes), its generalizability is often limited, and this can be tested using double-transgenic reporter lines. Lastly, due to the increasing importance of deep learning networks, a comparative approach can be adopted to study their applicability to biological datasets. In summary, we present a broad methodological overview ranging from image enhancement to segmentation with a mixed approach of experimental, simulated, and augmented datasets to assess and validate vascular segmentation using the zebrafish brain vasculature as an example. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. HIGHLIGHTS: Simulated, experimental, and augmented datasets provide an alternative to overcome the lack of segmentation gold standards and phantom models for zebrafish cerebrovascular segmentation. Direct generalization of a segmentation approach to the data for which it was not optimized (e.g., different transgenics or antibody stainings) should be treated with caution. Comparison of different deep learning segmentation methods can be used to assess their applicability to data. Here, we show that the zebrafish cerebral vasculature can be segmented with U-Net-based architectures, which outperform SegNet architectures.

Single-Input Multi-Output U-Net for Automated 2D Foetal Brain Segmentation of MR Images.

In this work, we develop the Single-Input Multi-Output U-Net (SIMOU-Net), a hybrid network for foetal brain segmentation inspired by the original U-Net fused with the holistically nested edge detection (HED) network. The SIMOU-Net is similar to the original U-Net but it has a deeper architecture and takes account of the features extracted from each side output. It acts similar to an ensemble neural network, however, instead of averaging the outputs from several independently trained models, which is computationally expensive, our approach combines outputs from a single network to reduce the variance of predications and generalization errors. Experimental results using 200 normal foetal brains consisting of over 11,500 2D images produced Dice and Jaccard coefficients of 94.2 ± 5.9% and 88.7 ± 6.9%, respectively. We further tested the proposed network on 54 abnormal cases (over 3500 images) and achieved Dice and Jaccard coefficients of 91.2 ± 6.8% and 85.7 ± 6.6%, respectively.