HVSMR-2.0: A 3D cardiovascular MR dataset for whole-heart segmentation in congenital heart disease.

Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image segmentation from a preoperative cardiovascular magnetic resonance (CMR) scan would enable creation of patient-specific 3D surface models of the heart, which have potential to improve surgical planning, enable surgical simulation, and allow automatic computation of quantitative metrics of heart function. However, there is no publicly available CMR dataset for whole-heart segmentation in patients with congenital heart disease. Here, we release the HVSMR-2.0 dataset, comprising 60 CMR scans alongside manual segmentation masks of the 4 cardiac chambers and 4 great vessels. The images showcase a wide range of heart defects and prior surgical interventions. The dataset also includes masks of required and optional extents of the great vessels, enabling fairer comparisons across algorithms. Detailed diagnoses for each subject are also provided. By releasing HVSMR-2.0, we aim to encourage development of robust segmentation algorithms and clinically relevant tools for congenital heart disease.

Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping.

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.

Automated segmentation of 3D cine cardiovascular magnetic resonance imaging.

Introduction: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish. Methods: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements. Results: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml). Discussion: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.

Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.

Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations.

Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs.

An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine's 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model's ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient's 3D spinal posture in the prone position from CT.

Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning.

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect in which the right ventricle and associated tricuspid valve (TV) alone support the circulation. TV failure is thus associated with heart failure, and the outcome of TV valve repair are currently poor. 3D echocardiography (3DE) can generate high-quality images of the valve, but segmentation is necessary for precise modeling and quantification. There is currently no robust methodology for rapid TV segmentation, limiting the clinical application of these technologies to this challenging population. We utilized a Fully Convolutional Network (FCN) to segment tricuspid valves from transthoracic 3DE. We trained on 133 3DE image-segmentation pairs and validated on 28 images. We then assessed the effect of varying inputs to the FCN using Mean Boundary Distance (MBD) and Dice Similarity Coefficient (DSC). The FCN with the input of an annular curve achieved a median DSC of 0.86 [IQR: 0.81-0.88] and MBD of 0.35 [0.23-0.4] mm for the merged segmentation and an average DSC of 0.77 [0.73-0.81] and MBD of 0.6 [0.44-0.74] mm for individual TV leaflet segmentation. The addition of commissural landmarks improved individual leaflet segmentation accuracy to an MBD of 0.38 [0.3-0.46] mm. FCN-based segmentation of the tricuspid valve from transthoracic 3DE is feasible and accurate. The addition of an annular curve and commissural landmarks improved the quality of the segmentations with MBD and DSC within the range of human inter-user variability. Fast and accurate FCN-based segmentation of the tricuspid valve in HLHS may enable rapid modeling and quantification, which in the future may inform surgical planning. We are now working to deploy this network for public use.

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease.

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the intermediate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incomplete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Compared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.

Characterizing the effect of multiple Fc glycan attributes on the effector functions and FcγRIIIa receptor binding activity of an IgG1 antibody.

N-linked Fc glycosylation of IgG1 monoclonal antibody therapeutics can directly influence their mechanism of action by impacting IgG effector functions such as antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC). Therefore, identification and detailed characterization of Fc glycan critical quality attributes (CQAs) provides important information for process design and control. A two-step approach was used to identify and characterize the Fc glycan CQAs for an IgG1 Mab with effector function. First, single factor experiments were performed to identify glycan critical quality attributes that influence ADCC and CDC activities. Next, a full-factorial design of experiment (DOE) to characterize the possible interactions and relative effect of these three glycan species on ADCC, CDC, and FcγRIIIa binding was employed. Additionally, the DOE data were used to develop models to predict ADCC, CDC, and FcγRIIIa binding of a given configuration of the three glycan species for this IgG1 molecule. The results demonstrate that for ADCC, afuco mono/bi has the largest effect, followed by HM and ß-gal, while FcγRIIIa binding is affected by afuco mono/bi and ß-gal. CDC, in contrast, is affected by ß-gal only. This type of glycan characterization and modeling can provide valuable information for development, manufacturing support and process improvements for IgG products that require effector function for efficacy. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1181-1192, 2016.

CHO cell production and sequence improvement in the 13C6FR1 anti-Ebola antibody.

From March 2014 through February 2015, the Ebola virus spread rapidly in West Africa, resulting in almost 30,000 infections and approximately 10,000 deaths. With no approved therapeutic options available, an experimental antibody cocktail known as ZMapp™ was administered to patients on a limited compassionate-use basis. The supply of ZMapp™ was highly constrained at the time because it was in preclinical development and a novel production system (tobacco plants) was being used for manufacturing. To increase the production of ZMapp™ for an uncertain future demand, a consortium was formed in the fall of 2014 to quickly manufacture these anti-Ebola antibodies in Chinese hamster ovary (CHO) cells using bioreactors for production at a scale appropriate for thousands of doses. As a result of the efforts of this consortium, valuable lessons were learned about the processing of the antibodies in a CHO-based system. One of the ZMapp™ cocktail antibodies, known as c13C6FR1, had been sequence-optimized in the framework region for production in tobacco and engineered as a chimeric antibody. When transfected into CHO cells with the unaltered sequence, 13C6FR1 was difficult to process. This report describes efforts to produce 13C6FR1 and the parental murine hybridoma sequence, 13C6mu, in CHO cells, and provides evidence for the insertion of a highly conserved framework amino acid that improved the physical properties necessary for high-level expression and purification. Furthermore, it describes the technical and logistical lessons learned that may be beneficial in the event of a future Ebola virus or other pandemic viral outbreaks where mAbs are considered potential therapeutics.

Interactive Whole-Heart Segmentation in Congenital Heart Disease.

We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.

A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs.

We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall.

Studying cerebral vasculature using structure proximity and graph kernels.

An approach to study population differences in cerebral vasculature is proposed. This is done by (1) extending the concept of encoding cerebral blood vessel networks as spatial graphs and (2) quantifying graph similarity in a kernel-based discriminant classifier setup. We argue that augmenting graph vertices with information about their proximity to selected brain structures adds discriminative information and consequently leads to a more expressive encoding. Using graph-kernels then allows us to quantify graph similarity in a principled way. To demonstrate our approach, we assess the hypothesis that gender differences manifest as variations in the architecture of cerebral blood vessels, an observation that previously had only been tested and confirmed for the Circle of Willis. Our results strongly support this hypothesis, i.e, we can demonstrate non-trivial, statistically significant deviations from random gender classification in a cross-validation setup on 40 healthy patients.

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

Geometric metamorphosis.

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient.

Deformable Image Registration of Sliding Organs Using Anisotropic Diffusive Regularization.

Traditional deformable image registration imposes a uniform smoothness constraint on the deformation field. This is not appropriate when registering images visualizing organs that slide relative to each other, and therefore leads to registration inaccuracies. In this paper, we present a deformation field regularization term that is based on anisotropic diffusion and accommodates the deformation field discontinuities that are expected when considering sliding motion. The registration algorithm was assessed first using artificial images of geometric objects. In a second validation, monomodal chest images depicting both respiratory and cardiac motion were generated using an anatomically-realistic software phantom and then registered. Registration accuracy was assessed based on the distances between corresponding segmented organ surfaces. Compared to an established diffusive regularization approach, the anisotropic diffusive regularization gave deformation fields that represented more plausible image correspondences, while giving rise to similar transformed moving images and comparable registration accuracy.

Investigation of degradation processes in IgG1 monoclonal antibodies by limited proteolysis coupled with weak cation-exchange HPLC.

A new cation-exchange high-performance liquid chromatography (HPLC) method that separates fragment antigen-binding (Fab) and fragment crystallizable (Fc) domains generated by the limited proteolysis of monoclonal antibodies (mAbs) was developed. This assay has proven to be suitable for studying complex degradation processes involving various immunoglobulin G1 (IgG1) molecules. Assignment of covalent degradations to specific regions of mAbs was facilitated by using Lys-C and papain to generate Fab and Fc fragments with unique, protease-dependent elution times. In particular, this method was useful for characterizing protein variants formed in the presence of salt under accelerated storage conditions. Two isoforms that accumulated during storage were readily identified as Fab-related species prior to mass-spectrometric analysis. Both showed reduced biological activity likely resulting from modifications within or in proximity of the complementarity-determining regions (CDRs). Utility of this assay was further illustrated in the work to characterize light-induced degradations in mAb formulations. In this case, a previously unknown Fab-related species which populated upon light exposure was observed. This species was well resolved from unmodified Fab, allowing for direct and high-purity fractionation. Mass-spectrometric analysis subsequently identified a histidine-related degradation product associated with the CDR2 of the heavy chain. In addition, the method was applied to assess the structural organization of a noncovalent IgG1 dimer. A new species corresponding to a Fab-Fab complex was found, implying that interactions between Fab domains were responsible for dimerization. Overall, the data presented demonstrate the suitability of this cation-exchange HPLC method for studying a wide range of covalent and noncovalent degradations in IgG1 mAbs.

Augmented reality image guidance during off-pump mitral valve replacement through the guiraudon universal cardiac introducer.

OBJECTIVE: : We report our experience with ultrasound augmented reality (US-AR) guidance for mitral valve prosthesis (MVP) implantation in the pig using off-pump, closed, beating intracardiac access through the Guiraudon Universal Cardiac Introducer attached to the left atrial appendage. METHODS: : Before testing US-AR guidance, a feasibility pilot study on nine pigs was performed using US alone. US-AR guidance, tested on a heart phantom, was subsequently used in three pigs (∼65 kg) using a tracked transesophageal echocardiography probe, augmented with registration of a 3D computed tomography scan, and virtual representation of the MVP and clip-delivering tool (Clipper); three pigs were used to test feature-based registration. RESULTS: : Navigation of the MVP was facilitated by the 3D anatomic display. AR displayed the MVP and the Clipper within the Atamai Viewer, with excellent accuracy for tool placement. Positioning the Clipper was hampered by the design of the MVP holder and Clipper. These limitations were well displayed by AR, which provided guidance for improved design of tools. CONCLUSIONS: : US-AR provided informative image guidance. It documented the flaws of the current implantation technology. This information could not be obtained by any other method of evaluation. These evaluations provided guidance for designing an integrated tool: combining an unobtrusive valve holder that allows the MVP to function properly as soon as positioned, and an anchoring system, with clips that can be released one at a time, and retracted if necessary, for optimal results. The portability of Real-time US-AR may prove to be the ideal practical image guidance system for all closed intracardiac interventions.

O-fucosylation of an antibody light chain: characterization of a modification occurring on an IgG1 molecule.

We describe the characterization of an O-fucosyl modification to a serine residue on the light chain of a recombinant, human IgG1 molecule expressed in Chinese hamster ovary (CHO) cells. Cation exchange chromatography (CEX) and hydrophobic interaction chromatography (HIC) were used to isolate a Fab population which was 146 Da heavier than the expected mass. Isolated Fab fragments were treated with a reducing agent to facilitate mass spectrometric analysis of the reduced light chain (LC) and fragment difficult (Fd). An antibody light chain with a net addition of 146 Da was detected by mass spectrometric analysis of the modified Fab. A light chain tryptic peptide in complementarity determining region-1 (CDR-1) was subsequently identified with a net addition of 146 Da by a peptide map. Results from a nanospray infusion of the modified peptide into a linear ion trap mass spectrometer with electron transfer dissociation (ETD) functionality indicated that the modified residue was a serine at position 30 in the light chain. Acid hydrolysis of the modified tryptic peptide followed by fluorescent labeling with 2-aminoanthranilic acid (2AA) and HPLC comparison with monosaccharide standards confirmed the presence of fucose on the light chain peptide. The presence of O-fucose on an antibody has not been previously reported. Currently, O-fucose has been described as occurring on mammalian proteins with amino acid sequence motifs associated with epidermal growth factor (EGF)-like repeats or thrombospondin type 1 repeats (TSRs). The amino acid sequence around the modified Ser in the IgG1 molecule does not conform to any known O-fucosylation sequence motif and thus is the first description of this type of modification on a nonconsensus sequence in a mammalian protein.

Image guidance for spinal facet injections using tracked ultrasound.

Anesthetic nerve blocks are a common therapy performed in hospitals around the world to alleviate acute and chronic pain. Tracking systems have shown considerable promise in other forms of therapy, but little has been done to apply this technology in the field of anesthesia. We are developing a guidance system for combining tracked needles with non-invasive ultrasound (US) and patient-specific geometric models. In experiments with phantoms two augmented reality (AR) guidance systems were compared to the exclusive use of US for lumbar facet injection therapy. Anesthetists and anesthesia residents were able to place needles within 0.57 mm of the intended targets using our AR systems compared to 5.77 mm using US alone. A preliminary cadaver study demonstrated the system was able to accurately place radio opaque dye on targets. The combination of real time US with tracked tools and AR guidance has the potential to replace CT and fluoroscopic guidance, thus reducing radiation dose to patients and clinicians, as well as reducing health care costs.

Disulfide connectivity of human immunoglobulin G2 structural isoforms.

In this communication we present the detailed disulfide structure of IgG2 molecules. The consensus structural model of human IgGs represents the hinge region positioned as a flexible linker connecting structurally isolated Fc and Fab domains. IgG2 molecules are organized differently from that model and exhibit multiple structural isoforms composed of (heavy chain-light chain-hinge) covalent complexes. We describe the precise connection of all the disulfide bridges and show that the IgG2 C H1 and C-terminal C L cysteine residues are either linked to each other or to the two upper hinge cysteine residues specific to the IgG2 subclass. A defined arrangement of these disulfide bridges is unique to each isoform. Mutation of a single cysteine residue in the hinge region eliminates these natural complexes. These results show that IgG2 structure is significantly different from the conventionally accepted immunoglobulin structural model and may help to explain some of the unique biological activity attributed only to this subclass.