A liver immune rheostat regulates CD8 T cell immunity in chronic HBV infection.

Chronic hepatitis B virus (HBV) infection affects 300 million patients worldwide1,2, in whom virus-specific CD8 T cells by still ill-defined mechanisms lose their function and cannot eliminate HBV-infected hepatocytes3-7. Here we demonstrate that a liver immune rheostat renders virus-specific CD8 T cells refractory to activation and leads to their loss of effector functions. In preclinical models of persistent infection with hepatotropic viruses such as HBV, dysfunctional virus-specific CXCR6+ CD8 T cells accumulated in the liver and, as a characteristic hallmark, showed enhanced transcriptional activity of cAMP-responsive element modulator (CREM) distinct from T cell exhaustion. In patients with chronic hepatitis B, circulating and intrahepatic HBV-specific CXCR6+ CD8 T cells with enhanced CREM expression and transcriptional activity were detected at a frequency of 12-22% of HBV-specific CD8 T cells. Knocking out the inhibitory CREM/ICER isoform in T cells, however, failed to rescue T cell immunity. This indicates that CREM activity was a consequence, rather than the cause, of loss in T cell function, further supported by the observation of enhanced phosphorylation of protein kinase A (PKA) which is upstream of CREM. Indeed, we found that enhanced cAMP-PKA-signalling from increased T cell adenylyl cyclase activity augmented CREM activity and curbed T cell activation and effector function in persistent hepatic infection. Mechanistically, CD8 T cells recognizing their antigen on hepatocytes established close and extensive contact with liver sinusoidal endothelial cells, thereby enhancing adenylyl cyclase-cAMP-PKA signalling in T cells. In these hepatic CD8 T cells, which recognize their antigen on hepatocytes, phosphorylation of key signalling kinases of the T cell receptor signalling pathway was impaired, which rendered them refractory to activation. Thus, close contact with liver sinusoidal endothelial cells curbs the activation and effector function of HBV-specific CD8 T cells that target hepatocytes expressing viral antigens by means of the adenylyl cyclase-cAMP-PKA axis in an immune rheostat-like fashion.

Phage anti-CBASS and anti-Pycsar nucleases subvert bacterial immunity.

The cyclic oligonucleotide-based antiphage signalling system (CBASS) and the pyrimidine cyclase system for antiphage resistance (Pycsar) are antiphage defence systems in diverse bacteria that use cyclic nucleotide signals to induce cell death and prevent viral propagation1,2. Phages use several strategies to defeat host CRISPR and restriction-modification systems3-10, but no mechanisms are known to evade CBASS and Pycsar immunity. Here we show that phages encode anti-CBASS (Acb) and anti-Pycsar (Apyc) proteins that counteract defence by specifically degrading cyclic nucleotide signals that activate host immunity. Using a biochemical screen of 57 phages in Escherichia coli and Bacillus subtilis, we discover Acb1 from phage T4 and Apyc1 from phage SBSphiJ as founding members of distinct families of immune evasion proteins. Crystal structures of Acb1 in complex with 3'3'-cyclic GMP-AMP define a mechanism of metal-independent hydrolysis 3' of adenosine bases, enabling broad recognition and degradation of cyclic dinucleotide and trinucleotide CBASS signals. Structures of Apyc1 reveal a metal-dependent cyclic NMP phosphodiesterase that uses relaxed specificity to target Pycsar cyclic pyrimidine mononucleotide signals. We show that Acb1 and Apyc1 block downstream effector activation and protect from CBASS and Pycsar defence in vivo. Active Acb1 and Apyc1 enzymes are conserved in phylogenetically diverse phages, demonstrating that cleavage of host cyclic nucleotide signals is a key strategy of immune evasion in phage biology.

A landmark-free morphometrics pipeline for high-resolution phenotyping: application to a mouse model of Down syndrome.

Characterising phenotypes often requires quantification of anatomical shape. Quantitative shape comparison (morphometrics) traditionally uses manually located landmarks and is limited by landmark number and operator accuracy. Here, we apply a landmark-free method to characterise the craniofacial skeletal phenotype of the Dp1Tyb mouse model of Down syndrome and a population of the Diversity Outbred (DO) mouse model, comparing it with a landmark-based approach. We identified cranial dysmorphologies in Dp1Tyb mice, especially smaller size and brachycephaly (front-back shortening), homologous to the human phenotype. Shape variation in the DO mice was partly attributable to allometry (size-dependent shape variation) and sexual dimorphism. The landmark-free method performed as well as, or better than, the landmark-based method but was less labour-intensive, required less user training and, uniquely, enabled fine mapping of local differences as planar expansion or shrinkage. Its higher resolution pinpointed reductions in interior mid-snout structures and occipital bones in both the models that were not otherwise apparent. We propose that this landmark-free pipeline could make morphometrics widely accessible beyond its traditional niches in zoology and palaeontology, especially in characterising developmental mutant phenotypes.

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.

OBJECTIVE: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools. METHODS: A prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning. RESULTS: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI-assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks. CONCLUSION: Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.

Mapping of truncated O-glycans in cancers of epithelial and non-epithelial origin.

BACKGROUND: Novel immunotherapies targeting cancer-associated truncated O-glycans Tn (GalNAcα-Ser/Thr) and STn (Neu5Acα2-6GalNacα-Ser/Thr) are promising strategies for cancer treatment. However, no comprehensive, antibody-based mapping of truncated O-glycans in tumours exist to guide drug development. METHODS: We used monoclonal antibodies to map the expression of truncated O-glycans in >700 tissue cores representing healthy and tumour tissues originating from breast, colon, lung, pancreas, skin, CNS and mesenchymal tissue. Patient-derived xenografts were used to evaluate Tn expression upon tumour engraftment. RESULTS: The Tn-antigen was highly expressed in breast (57%, n = 64), colorectal (51%, n = 140) and pancreatic (53%, n = 108) tumours, while STn was mainly observed in colorectal (80%, n = 140) and pancreatic (56%, n = 108) tumours. We observed no truncated O-glycans in mesenchymal tumours (n = 32) and low expression of Tn (5%, n = 87) and STn (1%, n = 75) in CNS tumours. No Tn-antigen was found in normal tissue (n = 124) while STn was occasionally observed in healthy gastrointestinal tissue. Surface expression of Tn-antigen was identified across several cancers. Tn and STn expression decreased with tumour grade, but not with cancer stage. Numerous xenografts maintained Tn expression. CONCLUSIONS: Surface expression of truncated O-glycans is limited to cancers of epithelial origin, making Tn and STn attractive immunological targets in the treatment of human carcinomas.

Viral transmission in honey bees and native bees, supported by a global black queen cell virus phylogeny.

In recent decades, we have realized that honey bee viruses are not, in fact, exclusive to honey bees. The potential impact of Apis-affiliated viruses on native pollinators is prompting concern. Our research addresses the issue of virus crossover between honey bees and native bees foraging in the same localities. We measured the presence of black queen cell virus (BQCV), deformed wing virus (DWV) and sacbrood virus (SBV) in managed Apis mellifera (honey bees) and native Andrena spp. (subgenus Melandrena) bee populations in five commercial orchards. We identified viral presence across sites and bees and related these data to measures of bee community diversity. All viruses were found in both managed and native bees, and BQCV was the most common virus in each. To establish evidence for viral crossover between taxa, we undertook an additional examination of BQCV where 74 samples were sequenced and placed in a global phylogenic framework of hundreds of BQCV strains. We demonstrate pathogen sharing across managed honey bees and distantly related wild bees. This phylogenetic analysis contributes to growing evidence for host switching and places local incidence patterns in a worldwide context, revealing multispecies viral transmission.

Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI.

PURPOSE: Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision. THEORY AND METHODS: The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast-enhanced magnetic resonance imaging. RESULTS: Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast-to-noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95). CONCLUSION: The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise. Magn Reson Med 77:2414-2423, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Supervoxels for Graph Cuts-Based Deformable Image Registration Using Guided Image Filtering.

In this work we propose to combine a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for 3D deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to 2D applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation, combined with graph cuts-based optimization can be applied to 3D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model 'sliding motion'. Applying this method to lung image registration, results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available Computed Tomography lung image dataset (www.dir-lab.com) leads to the observation that our new approach compares very favorably with state-of-the-art in continuous and discrete image registration methods achieving Target Registration Error of 1.16mm on average per landmark.

Long-Term Warming Alters Carbohydrate Degradation Potential in Temperate Forest Soils.

As Earth's climate warms, soil carbon pools and the microbial communities that process them may change, altering the way in which carbon is recycled in soil. In this study, we used a combination of metagenomics and bacterial cultivation to evaluate the hypothesis that experimentally raising soil temperatures by 5°C for 5, 8, or 20 years increased the potential for temperate forest soil microbial communities to degrade carbohydrates. Warming decreased the proportion of carbohydrate-degrading genes in the organic horizon derived from eukaryotes and increased the fraction of genes in the mineral soil associated with Actinobacteria in all studies. Genes associated with carbohydrate degradation increased in the organic horizon after 5 years of warming but had decreased in the organic horizon after warming the soil continuously for 20 years. However, a greater proportion of the 295 bacteria from 6 phyla (10 classes, 14 orders, and 34 families) isolated from heated plots in the 20-year experiment were able to depolymerize cellulose and xylan than bacterial isolates from control soils. Together, these findings indicate that the enrichment of bacteria capable of degrading carbohydrates could be important for accelerated carbon cycling in a warmer world. IMPORTANCE: The massive carbon stocks currently held in soils have been built up over millennia, and while numerous lines of evidence indicate that climate change will accelerate the processing of this carbon, it is unclear whether the genetic repertoire of the microbes responsible for this elevated activity will also change. In this study, we showed that bacteria isolated from plots subject to 20 years of 5°C of warming were more likely to depolymerize the plant polymers xylan and cellulose, but that carbohydrate degradation capacity is not uniformly enriched by warming treatment in the metagenomes of soil microbial communities. This study illustrates the utility of combining culture-dependent and culture-independent surveys of microbial communities to improve our understanding of the role changing microbial communities may play in soil carbon cycling under climate change.

Spatio-temporal pharmacokinetic model based registration of 4D PET neuroimaging data.

In dynamic positron emission tomography (PET) neuroimaging studies, where scan durations often exceed 1h, registration of motion-corrupted dynamic PET images is necessary in order to maintain the integrity of the physiological, pharmacological, or biochemical information derived from the tracer kinetic analysis of the scan. In this work, we incorporate a pharmacokinetic model, which is traditionally used to analyse PET data following any registration, into the registration process itself in order to allow for a groupwise registration of the temporal time frames. The new method is shown to achieve smaller registration errors and improved kinetic parameter estimates on validation data sets when compared with image similarity based registration approaches. When applied to measured clinical data from 10 healthy subjects scanned with [(11)C]-(+)-PHNO (a dopamine D3/D2 receptor tracer), it reduces the intra-class variability on the receptor binding outcome measure, further supporting the improvements in registration accuracy. Our method incorporates a generic tracer kinetic model which makes it applicable to different PET radiotracers to remove motion artefacts and increase the integrity of dynamic PET studies.

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer.

The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review.

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.

Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation.

OBJECTIVES: Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction. MATERIALS AND METHODS: Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds. RESULTS: The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose. CONCLUSIONS: Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis.

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this article, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework produces high quality reconstructions and segmentations, leading to undersampling factors that are optimised on a scan-by-scan basis. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform simulations of radial k-space acquisitions using in-vivo cine CMR data from 270 subjects from the UK Biobank (with synthetic phase) and in-vivo cine CMR data from 16 healthy subjects (with real phase). The results demonstrate that the optimal undersampling factor varies for different subjects by approximately 1 to 2 seconds per slice. We show that our method can produce quality-controlled images in a mean scan time reduced from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to lie within 5% mean absolute difference.

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.

Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.

Extended reality for procedural planning and guidance in structural heart disease - a review of the state-of-the-art.

Extended reality (XR), which encompasses virtual, augmented and mixed reality, is an emerging medical imaging display platform which enables intuitive and immersive interaction in a three-dimensional space. This technology holds the potential to enhance understanding of complex spatial relationships when planning and guiding cardiac procedures in congenital and structural heart disease moving beyond conventional 2D and 3D image displays. A systematic review of the literature demonstrates a rapid increase in publications describing adoption of this technology. At least 33 XR systems have been described, with many demonstrating proof of concept, but with no specific mention of regulatory approval including some prospective studies. Validation remains limited, and true clinical benefit difficult to measure. This review describes and critically appraises the range of XR technologies and its applications for procedural planning and guidance in structural heart disease while discussing the challenges that need to be overcome in future studies to achieve safe and effective clinical adoption.

Colorimetric Sensor Reading and Illumination Correction via Multi-Task Deep-Learning.

Colorimetric sensors represent an accessible and sensitive nanotechnology for rapid and accessible measurement of a substance's properties (e.g., analyte concentration) via color changes. Although colorimetric sensors are widely used in healthcare and laboratories, interpretation of their output is performed either by visual inspection or using cameras in highly controlled illumination set-ups, limiting their usage in end-user applications, with lower resolutions and altered light conditions. For that purpose, we implement a set of image processing and deep-learning (DL) methods that correct for non-uniform illumination alterations and accurately read the target variable from the color response of the sensor. Methods that perform both tasks independently vs. jointly in a multi-task model are evaluated. Video recordings of colorimetric sensors measuring temperature conditions were collected to build an experimental reference dataset. Sensor images were augmented with non-uniform color alterations. The best-performing DL architecture disentangles the luminance, chrominance, and noise via separate decoders and integrates a regression task in the latent space to predict the sensor readings, achieving a mean squared error (MSE) performance of 0.811±0.074[°C] and r2=0.930±0.007, under strong color perturbations, resulting in an improvement of 1.26[°C] when compared to the MSE of the best performing method with independent denoising and regression tasks.Clinical Relevance- The proposed methodology aims to improve the accuracy of colorimetric sensor reading and their large-scale accessibility as point-of-care diagnostic and continuous health monitoring devices, in altered illumination conditions.

Engaging an HIV vaccine target through the acquisition of low B cell affinity.

Low affinity is common for germline B cell receptors (BCR) seeding development of broadly neutralizing antibodies (bnAbs) that engage hypervariable viruses, including HIV. Antibody affinity selection is also non-homogenizing, insuring the survival of low affinity B cell clones. To explore whether this provides a natural window for expanding human B cell lineages against conserved vaccine targets, we deploy transgenic mice mimicking human antibody diversity and somatic hypermutation (SHM) and immunize with simple monomeric HIV glycoprotein envelope immunogens. We report an immunization regimen that focuses B cell memory upon the conserved CD4 binding site (CD4bs) through both conventional affinity maturation and reproducible expansion of low affinity BCR clones with public patterns in SHM. In the latter instance, SHM facilitates target acquisition by decreasing binding strength. This suggests that permissive B cell selection enables the discovery of antibody epitopes, in this case an HIV bnAb site.

Targeting Solid Cancers with a Cancer-Specific Monoclonal Antibody to Surface Expressed Aberrantly O-glycosylated Proteins.

The lack of antibodies with sufficient cancer selectivity is currently limiting the treatment of solid tumors by immunotherapies. Most current immunotherapeutic targets are tumor-associated antigens that are also found in healthy tissues and often do not display sufficient cancer selectivity to be used as targets for potent antibody-based immunotherapeutic treatments, such as chimeric antigen receptor (CAR) T cells. Many solid tumors, however, display aberrant glycosylation that results in expression of tumor-associated carbohydrate antigens that are distinct from healthy tissues. Targeting aberrantly glycosylated glycopeptide epitopes within existing or novel glycoprotein targets may provide the cancer selectivity needed for immunotherapy of solid tumors. However, to date only a few such glycopeptide epitopes have been targeted. Here, we used O-glycoproteomics data from multiple cell lines to identify a glycopeptide epitope in CD44v6, a cancer-associated CD44 isoform, and developed a cancer-specific mAb, 4C8, through a glycopeptide immunization strategy. 4C8 selectively binds to Tn-glycosylated CD44v6 in a site-specific manner with low nanomolar affinity. 4C8 was shown to be highly cancer specific by IHC of sections from multiple healthy and cancerous tissues. 4C8 CAR T cells demonstrated target-specific cytotoxicity in vitro and significant tumor regression and increased survival in vivo. Importantly, 4C8 CAR T cells were able to selectively kill target cells in a mixed organotypic skin cancer model having abundant CD44v6 expression without affecting healthy keratinocytes, indicating tolerability and safety.

Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.

Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.