Two truncating variants in FANCC and breast cancer risk

Fanconi anemia (FA) is a genetically heterogeneous disorder with 22 disease-causing genes reported to date. In some FA genes, monoallelic mutations have been found to be associated with breast cancer risk, while the risk associations of others remain unknown. The gene for FA type C, FANCC, has been proposed as a breast cancer susceptibility gene based on epidemiological and sequencing studies. We used the Oncoarray project to genotype two truncating FANCC variants (p.R185X and p.R548X) in 64,760 breast cancer cases and 49,793 controls of European descent. FANCC mutations were observed in 25 cases (14 with p.R185X, 11 with p.R548X) and 26 controls (18 with p.R185X, 8 with p.R548X). There was no evidence of an association with the risk of breast cancer, neither overall (odds ratio 0.77, 95% CI 0.44-1.33, p = 0.4) nor by histology, hormone receptor status, age or family history. We conclude that the breast cancer risk association of these two FANCC variants, if any, is much smaller than for BRCA1, BRCA2 or PALB2 mutations. If this applies to all truncating variants in FANCC it would suggest there are differences between FA genes in their roles on breast cancer risk and demonstrates the merit of large consortia for clarifying risk associations of rare variants.Peer reviewe

Left atrial wall segmentation from CT for radiofrequency catheter ablation planning

© Springer International Publishing Switzerland 2016. Atrial fibrillation is the most common cardiac arrhythmia and a major cause of ischemic stroke. It is believed that measurements of the thickness of a patient’s left atrial wall can improve understanding of the patient’s disease state, as well as assist in treatment planning for radiofrequency catheter ablation. Left atrial wall thickness can be measured and visualized from segmented contrast-enhanced cardiac CT images, but segmentation itself is challenging. Here we present a pipeline for segmenting the left atrial wall, using a hierarchical constraint structure in order to distinguish between the atrial wall and other muscular structures. Using this approach, the left atrial wall was successfully differentiated from adjacent structures such as the aortic wall. The method was compared to manual segmentation on ten clinical CT images of patients undergoing radiofrequency catheter ablation for atrial fibrillation. Similarity between the methods, by Dice coefficient, was found to be 0.79, and the rMSE of the epicardial segmentation was found to be 0.86 mm. A roadmap to automation for clinical translation is also presented

Cyclic Continuous Max-Flow: A Third Paradigm in Generating Local Phase Shift Maps in MRI

© 2017 IEEE. Sensitivity to phase deviations in MRI forms the basis of a variety of techniques, including magnetic susceptibility weighted imaging and chemical shift imaging. Current phase processing techniques fall into two families: those which process the complex image data with magnitude and phase coupled, and phase unwrapping-based techniques that first linearize the phase topology across the image. However, issues, such as low signal and the existence of phase poles, can lead both methods to experience error. Cyclic continuous max-flow (CCMF) phase processing uses primal-dual-variational optimization over a cylindrical manifold, which represent the inherent topology of phase images, increasing its robustness to these issues. CCMF represents a third distinct paradigm in phase processing, being the only technique equipped with the inherent topology of phase. CCMF is robust and efficient with at least comparable accuracy as the prior paradigms

The semiotics of medical image Segmentation

© 2017 Elsevier B.V. As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical image segmentation. Taking advantage of Peircean semiotic traditions and new philosophical inquiry into the structure and quality of metaphors, we can construct a unified framework for the interpretation of medical image segmentation as a sign exchange in which each sign acts as an interface metaphor. This allows for a notion of finite semiosis, described through a schematic medium, that can rigorously describe how clinicians and computers interpret the signs mediating their interaction. Altogether, this framework provides a unified approach to the understanding and development of medical image segmentation interfaces

Single slice US-MRI registration for neurosurgical MRI-guided US

© 2016 SPIE. Image-based ultrasound to magnetic resonance image (US-MRI) registration can be an invaluable tool in image-guided neuronavigation systems. State-of-the-art commercial and research systems utilize image-based registration to assist in functions such as brain-shift correction, image fusion, and probe calibration. Since traditional US-MRI registration techniques use reconstructed US volumes or a series of tracked US slices, the functionality of this approach can be compromised by the limitations of optical or magnetic tracking systems in the neurosurgical operating room. These drawbacks include ergonomic issues, line-of-sight/magnetic interference, and maintenance of the sterile field. For those seeking a US vendor-agnostic system, these issues are compounded with the challenge of instrumenting the probe without permanent modification and calibrating the probe face to the tracking tool. To address these challenges, this paper explores the feasibility of a real-time US-MRI volume registration in a small virtual craniotomy site using a single slice. We employ the Linear Correlation of Linear Combination (LC2) similarity metric in its patch-based form on data from MNI\u27s Brain Images for Tumour Evaluation (BITE) dataset as a PyCUDA enabled Python module in Slicer. By retaining the original orientation information, we are able to improve on the poses using this approach. To further assist the challenge of US-MRI registration, we also present the BOXLC2 metric which demonstrates a speed improvement to LC2, while retaining a similar accuracy in this context

An iterative closest point framework for ultrasound calibration

© Springer International Publishing Switzerland 2015. We introduce an Iterative Closest Point framework for ultrasound calibration based on a hollow-line phantom. The main novelty of our approach is the application of a hollow-tube fiducial made from hyperechoic material, which allows for highly accurate fiducial localization via both manual and automatic segmentation. By reducing fiducial localization error, this framework is able to achieve sub-millimeter target registration error. The calibration phantom introduced can be manufactured inexpensively and precisely. Using aMonte Carlo approach, our calibration framework achieved 0.5mm mean target registration error, with a standard deviation of 0.24 mm, using 12 or more tracked ultrasound images. This suggests that our framework is approaching the accuracy limit imposed by the tracking device used

Shape complexes in continuous max-flow segmentation

© 2016 SPIE. Optimization-based segmentation approaches deriving from discrete graph-cuts and continuous max-flow have become increasingly nuanced, allowing for topological and geometric constraints on the resulting segmentation while retaining global optimality. However, these two considerations, topological and geometric, have yet to be combined in a unified manner. This paper presents the concept of shape complexes, which combine geodesic star convexity with extendable continuous max-flow solvers. These shape complexes allow more complicated shapes to be created through the use of multiple labels and super-labels, with geodesic star convexity governed by a topological ordering. These problems can be optimized using extendable continuous max-flow solvers. Previous work required computationally expensive co-ordinate system warping which are ill-defined and ambiguous in the general case. These shape complexes are validated in a set of synthetic images as well as atrial wall segmentation from contrast-enhanced CT. Shape complexes represent a new, extendable tool alongside other continuous max-flow methods that may be suitable for a wide range of medical image segmentation problems

Registration of 3D shapes under anisotropic scaling: Anisotropic-scaled iterative closest point algorithm

© 2015, CARS. Purpose: Several medical imaging modalities exhibit inherent scaling among the acquired data: The scale in an ultrasound image varies with the speed of sound and the scale of the range data used to reconstruct organ surfaces is subject to the scanner distance. In the context of surface-based registration, these scaling factors are often assumed to be isotropic, or as a known prior. Accounting for such anisotropies in scale can potentially dramatically improve registration and calibrations procedures that are essential for robust image-guided interventions. Methods: We introduce an extension to the ordinary iterative closest point (ICP) algorithm, solving for the similarity transformation between point-sets comprising anisotropic scaling followed by rotation and translation. The proposed anisotropic-scaled ICP (ASICP) incorporate a novel use of Mahalanobis distance to establish correspondence and a new solution for the underlying registration problem. The derivation and convergence properties of ASICP are presented, and practical implementation details are discussed. Because the ASICP algorithm is independent of shape representation and feature extraction, it is generalizable for registrations involving scaling. Results: Experimental results involving the ultrasound calibration, registration of partially overlapping range data, whole surfaces, as well as multi-modality surface data (intraoperative ultrasound to preoperative MR) show dramatic improvement in fiducial registration error. Conclusion: We present a generalization of the ICP algorithm, solving for a similarity transform between two point-sets by means of anisotropic scales, followed by rotation and translation. Our anisotropic-scaled ICP algorithm shares many traits with the ordinary ICP, including guaranteed convergence, independence of shape representation, and general applicability

Mixed reality ultrasound guidance system: a case study in system development and a cautionary tale

© 2017, CARS. Purpose: Real-time ultrasound has become a crucial aspect of several image-guided interventions. One of the main constraints of such an approach is the difficulty in interpretability of the limited field of view of the image, a problem that has recently been addressed using mixed reality, such as augmented reality and augmented virtuality. The growing popularity and maturity of mixed reality has led to a series of informal guidelines to direct development of new systems and to facilitate regulatory approval. However, the goals of mixed reality image guidance systems and the guidelines for their development have not been thoroughly discussed. The purpose of this paper is to identify and critically examine development guidelines in the context of a mixed reality ultrasound guidance system through a case study. Methods: A mixed reality ultrasound guidance system tailored to central line insertions was developed in close collaboration with an expert user. This system outperformed ultrasound-only guidance in a novice user study and has obtained clearance for clinical use in humans. A phantom study with 25 experienced physicians was carried out to compare the performance of the mixed reality ultrasound system against conventional ultrasound-only guidance. Despite the previous promising results, there was no statistically significant difference between the two systems. Results: Guidelines for developing mixed reality image guidance systems cannot be applied indiscriminately. Each design decision, no matter how well justified, should be the subject of scientific and technical investigation. Iterative and small-scale evaluation can readily unearth issues and previously unknown or implicit system requirements. Conclusions: We recommend a wary eye in development of mixed reality ultrasound image guidance systems emphasizing small-scale iterative evaluation alongside system development. Ultimately, we recommend that the image-guided intervention community furthers and deepens this discussion into best practices in developing image-guided interventions