Cardiovascular Magnetic Resonance Imaging in Experimental Models

Cardiovascular magnetic resonance (CMR) imaging is the modality of choice for clinical studies of the heart and vasculature, offering detailed images of both structure and function with high temporal resolution

Semi-Automatic segmentation of multiple mouse embryos in MR images

<p>Abstract</p> <p>Background</p> <p>The motivation behind this paper is to aid the automatic phenotyping of mouse embryos, wherein multiple embryos embedded within a single tube were scanned using Magnetic Resonance Imaging (MRI).</p> <p>Results</p> <p>Our algorithm, a modified version of the simplex deformable model of Delingette, addresses various issues with deformable models including initialization and inability to adapt to boundary concavities. In addition, it proposes a novel technique for automatic collision detection of multiple objects which are being segmented simultaneously, hence avoiding major leaks into adjacent neighbouring structures. We address the initialization problem by introducing balloon forces which expand the initial spherical models close to the true boundaries of the embryos. This results in models which are less sensitive to initial minimum of two fold after each stage of deformation. To determine collision during segmentation, our unique collision detection algorithm finds the intersection between binary masks created from the deformed models after every few iterations of the deformation and modifies the segmentation parameters accordingly hence avoiding collision.</p> <p>We have segmented six tubes of three dimensional MR images of multiple mouse embryos using our modified deformable model algorithm. We have then validated the results of the our semi-automatic segmentation versus manual segmentation of the same embryos. Our Validation shows that except paws and tails we have been able to segment the mouse embryos with minor error.</p> <p>Conclusions</p> <p>This paper describes our novel multiple object segmentation technique with collision detection using a modified deformable model algorithm. Further, it presents the results of segmenting magnetic resonance images of up to 32 mouse embryos stacked in one gel filled test tube and creating 32 individual masks.</p

Multi-Scale Characterization of the PEPCK-Cmus Mouse through 3D Cryo-Imaging

We have developed, for the Case 3D Cryo-imaging system, a specialized, multiscale visualization scheme which provides color-rich volume rendering and multiplanar reformatting enabling one to visualize an entire mouse and zoom in to organ, tissue, and microscopic scales. With this system, we have anatomically characterized, in 3D, from whole animal to tissue level, a transgenic mouse and compared it with its control. The transgenic mouse overexpresses the cytosolic form of phosphoenolpyruvate carboxykinase (PEPCK-C) in its skeletal muscle and is capable of greatly enhanced physical endurance and has a longer life-span and reproductive life as compared to control animals. We semiautomatically analyzed selected organs such as kidney, heart, adrenal gland, spleen, and ovaries and found comparatively enlarged heart, much less visceral, subcutaneous, and pericardial adipose tissue, and higher tibia-to-femur ratio in the transgenic animal. Microscopically, individual skeletal muscle fibers, fine mesenteric blood vessels, and intestinal villi, among others, were clearly seen

Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images

Three studies have been carried out to investigate new approaches to efficient image segmentation and anomaly detection. The first study investigates the use of deep learning in patch based segmentation. Current approaches to patch based segmentation use low level features such as the sum of squared differences between patches. We argue that better segmentation can be achieved by harnessing the power of deep neural networks. Currently these networks make extensive use of convolutional layers. However, we argue that in the context of patch based segmentation, convolutional layers have little advantage over the canonical artificial neural network architecture. This is because a patch is small, and does not need decomposition and thus will not benefit from convolution. Instead, we make use of the canonical architecture in which neurons only compute dot products, but also incorporate modern techniques of deep learning. The resulting classifier is much faster and less memory-hungry than convolution based networks. In a test application to the segmentation of hippocampus in human brain MR images, we significantly outperformed prior art with a median Dice score up to 90.98% at a near real-time speed (<1s). The second study is an investigation into mouse phenotyping, and develops a high-throughput framework to detect morphological abnormality in mouse embryo micro-CT images. Existing work in this line is centred on, either the detection of phenotype-specific features or comparative analytics. The former approach lacks generality and the latter can often fail, for example, when the abnormality is not associated with severe volume variation. Both these approaches often require image segmentation as a pre-requisite, which is very challenging when applied to embryo phenotyping. A new approach to this problem in which non-rigid registration is combined with robust principal component analysis (RPCA), is proposed. The new framework is able to efficiently perform abnormality detection in a batch of images. It is sensitive to both volumetric and non-volumetric variations, and does not require image segmentation. In a validation study, it successfully distinguished the abnormal VSD and polydactyly phenotypes from the normal, respectively, at 85.19% and 88.89% specificities, with 100% sensitivity in both cases. The third study investigates the RPCA technique in more depth. RPCA is an extension of PCA that tolerates certain levels of data distortion during feature extraction, and is able to decompose images into regular and singular components. It has previously been applied to many computer vision problems (e.g. video surveillance), attaining excellent performance. However these applications commonly rest on a critical condition: in the majority of images being processed, there is a background with very little variation. By contrast in biomedical imaging there is significant natural variation across different images, resulting from inter-subject variability and physiological movements. Non-rigid registration can go some way towards reducing this variance, but cannot eliminate it entirely. To address this problem we propose a modified framework (RPCA-P) that is able to incorporate natural variation priors and adjust outlier tolerance locally, so that voxels associated with structures of higher variability are compensated with a higher tolerance in regularity estimation. An experimental study was applied to the same mouse embryo micro-CT data, and notably improved the detection specificity to 94.12% for the VSD and 90.97% for the polydactyly, while maintaining the sensitivity at 100%.Open Acces

ASPP2 deficiency causes features of 1q41q42 microdeletion syndrome

Chromosomal abnormalities are implicated in a substantial number of human developmental syndromes, but for many such disorders little is known about the causative genes. The recently described 1q41q42 microdeletion syndrome is characterized by characteristic dysmorphic features, intellectual disability and brain morphological abnormalities, but the precise genetic basis for these abnormalities remains unknown. Here, our detailed analysis of the genetic abnormalities of 1q41q42 microdeletion cases identified TP53BP2, which encodes apoptosis-stimulating protein of p53 2 (ASPP2), as a candidate gene for brain abnormalities. Consistent with this, Trp53bp2-deficient mice show dilation of lateral ventricles resembling the phenotype of 1q41q42 microdeletion patients. Trp53bp2 deficiency causes 100% neonatal lethality in the C57BL/6 background associated with a high incidence of neural tube defects and a range of developmental abnormalities such as congenital heart defects, coloboma, microphthalmia, urogenital and craniofacial abnormalities. Interestingly, abnormalities show a high degree of overlap with 1q41q42 microdeletion-associated abnormalities. These findings identify TP53BP2 as a strong candidate causative gene for central nervous system (CNS) defects in 1q41q42 microdeletion syndrome, and open new avenues for investigation of the mechanisms underlying CNS abnormalities

Assembling models of embryo development: Image analysis and the construction of digital atlases

Digital atlases of animal development provide a quantitative description of morphogenesis, opening the path toward processes modeling. Prototypic atlases offer a data integration framework where to gather information from cohorts of individuals with phenotypic variability. Relevant information for further theoretical reconstruction includes measurements in time and space for cell behaviors and gene expression. The latter as well as data integration in a prototypic model, rely on image processing strategies. Developing the tools to integrate and analyze biological multidimensional data are highly relevant for assessing chemical toxicity or performing drugs preclinical testing. This article surveys some of the most prominent efforts to assemble these prototypes, categorizes them according to salient criteria and discusses the key questions in the field and the future challenges toward the reconstruction of multiscale dynamics in model organisms

Identification of cardiac malformations in mice lacking Ptdsr using a novel high-throughput magnetic resonance imaging technique

BACKGROUND: Congenital heart defects are the leading non-infectious cause of death in children. Genetic studies in the mouse have been crucial to uncover new genes and signaling pathways associated with heart development and congenital heart disease. The identification of murine models of congenital cardiac malformations in high-throughput mutagenesis screens and in gene-targeted models is hindered by the opacity of the mouse embryo. RESULTS: We developed and optimized a novel method for high-throughput multi-embryo magnetic resonance imaging (MRI). Using this approach we identified cardiac malformations in phosphatidylserine receptor (Ptdsr) deficient embryos. These included ventricular septal defects, double-outlet right ventricle, and hypoplasia of the pulmonary artery and thymus. These results indicate that Ptdsr plays a key role in cardiac development. CONCLUSIONS: Our novel multi-embryo MRI technique enables high-throughput identification of murine models for human congenital cardiopulmonary malformations at high spatial resolution. The technique can be easily adapted for mouse mutagenesis screens and, thus provides an important new tool for identifying new mouse models for human congenital heart diseases

High-throughput transgenic mouse phenotyping using microscopic-MRI

With the completion of the human genome sequence in 2003, efforts have shifted towards elucidating gene function. Such phenotypic investigations are aided by advances in techniques for genetic modification of mice, with whom we share ~99% of genes. Mice are key models for both examination of basic gene function and translational study of human conditions. Furthering these efforts, ambitious programmes are underway to produce knockout mice for the ~25,000 mouse genes. In the coming years, methods to rapidly phenotype mouse morphology will be in great demand. This thesis demonstrates the development of non-invasive microscopic magnetic resonance imaging (\muMRI) methods for high-resolution ex-vivo phenotyping of mouse embryo and mouse brain morphology. It then goes on to show the application of computational atlasing techniques to these datasets, enabling automated analysis of phenotype. First, the issue of image quality in high-throughput embryo MRI was addressed. After investigating preparation and imaging parameters, substantial gains in signal- and contrast-to-noise were achieved. This protocol was applied to a study of Chd7+/- mice (a model of CHARGE syndrome), identifying cardiac defects. Combining this protocol with automated segmentation-propagation techniques, phenotypic differences were shown between three groups of mice in a volumetric analysis involving a number of organ systems. Focussing on the mouse brain, the optimal preparation and imaging parameters to maximise image quality and structural contrast were investigated, producing a high-resolution in-skull imaging protocol. Enhanced delineation of hippocampal and cerebellar structures was observed, correlating well to detailed histological comparisons. Subsequently this protocol was applied to a phenotypic investigation of the Tc1 model of Down syndrome. Using both visual inspection and automated, tensor based morphometry, novel phenotypic findings were identified in brain and inner ear structures. It is hoped that a combination of \muMRI with computational analysis techniques, as presented in this work, may help ease the burden of current phenotyping efforts

Maternal high-fat diet interacts with embryonic Cited2 genotype to reduce Pitx2c expression and enhance penetrance of left–right patterning defects

Deficiency of the transcription factor Cited2 in mice results in cardiac malformation, adrenal agenesis, neural tube, placental defects and partially penetrant cardiopulmonary laterality defects resulting from an abnormal Nodal->Pitx2c pathway. Here we show that a maternal high-fat diet more than doubles the penetrance of laterality defects and, surprisingly, induces palatal clefting in Cited2-deficient embryos. Both maternal diet and Cited2 deletion reduce embryo weight and kidney and thymus volume. Expression profiling identified 40 embryonic transcripts including Pitx2 that were significantly affected by embryonic genotype-maternal diet interaction. We show that a high-fat diet reduces Pitx2c levels >2-fold in Cited2-deficient embryos. Taken together, these results define a novel interaction between maternal high-fat diet and embryonic Cited2 deficiency that affects Pitx2c expression and results in abnormal laterality. They suggest that appropriate modifications of maternal diet may prevent such defects in humans