The development of a protocol for the analysis of genetic expression through «differential display», as a means to reducing the number of false positives

Entre los métodos empleados para los análisis de la expresión de genes, el método de "differentialdisplay" ha sido ampliamente utilizado y, a pesar del uso extendido de los "microarrays", es aún unmétodo válido para el análisis con muestras cuyo transcriptoma es desconocido. Con el objeto de reducirel elevado número de falsos positivos que genera esta técnica, hemos optimizado el protocolo parareducir la posibilidad de generar falsos positivos. En primer lugar, hemos marcado radiactivamente elcebador oligo-dT con lo que los fragmentos de DNA identificados son extremos 3'-UTR de RNAm. Pormuestra hemos realizado dos transcripciones inversas y dos reacciones de PCR en cada una de ellas. Paraseleccionar un fragmento de DNA, debía estar diferencialmente expresado en las 4 reacciones de PCR.Por último, todos los fragmentos fueron clonados y secuenciados por triplicado. Estas modificacionesal protocolo nos ha permitido identificar 5 genes expresados diferencialmente entre células epitelialesde intestino en estado proliferativo y diferenciado.The analysis of genetic expression, the differential display (DD) method has been widely used, but inspite of the extensive use of the «microarrays» method, it is still to be considered as a valid methodfor the analysis of samples whose transcriptone is not known. In this work, an attempt has been madeto reduce the high number of false positives generated by this technique by optimising method protocol.As a preliminary step, we radioactively marked the oligo dT primer with which the fragments ofidentified DNA were extreme 3'-UTR of mRNA. For each sample two inverse transcriptions and twoPCR reactions were performed. Only fragments of DNA that are expressed differentially in all 4 PCRreactions should be selected. Finally, all of the fragments were cloned and sequenced in triplicate. Theseprotocol modifications have allowed us to identify 5 differentially expressed genes, in intestinal epithelialcells in both proliferative and differentiated states

Intracranial Aneurysm Detection from 3D Vascular Mesh Models with Ensemble Deep Learning

Intracranial aneurysm rupture can cause a serious stroke, which is related to the decline of daily life ability of the elderly. Although deep learning is now the most successful solution for organ detection, it requires myriads of training data, consistent of the image format, and a balanced sample distribution. This work presents an innovative representation of intracranial aneurysm detection as a shape analysis problem rather than a computer vision problem. We detected intracranial aneurysms in 3D cerebrovascular mesh models after segmentation of the brain vessels from the medical images, which can overcome the barriers of data format and data distribution, serving both clinical and screening purposes. Additionally, we propose a transferable multi-model ensemble (MMEN) architecture to detect intracranial aneurysms from cerebrovascular mesh models with limited data. To obtain a well-defined convolution operator, we use a global seamless parameterization converting a 3D cerebrovascular mesh model to a planar flat-torus. In the architecture, we transfer the planar flat-torus presentation abilities of three GoogleNet Inception V3 models, which were pre-trained on the ImageNet database, to characterize the intracranial aneurysms with local and global geometric features such as Gaussian curvature (GC), shape diameter function (SDF) and wave kernel signature (WKS), respectively. We jointly utilize all three models to detect aneurysms with adaptive weights learning based on back propagation. The experimental results on the 121 models show that our proposed method can achieve detection accuracy of 95.1% with 94.7% F1-score and 94.8% sensitivity, which is as good as the state-of-art work but is applicable to inhomogeneous image modalities and smaller datasets

Information theoretic measurement of blood flow complexity in vessels and aneurysms: Interlacing complexity index

Haemodynamics is believed to be a crucial factor in the aneurysm formation, evolution and eventual rupture. The 3D blood flow is typically derived by computational fluid dynamics (CFD) from patient-specific models obtained from angiographic images. Typical quantitative haemodynamic indices are local. Some qualitative classifications of global haemodynamic features have been proposed. However these classifications are subjective, depending on the operator visual inspection. In this work we introduce an information theoretic measurement of the blood flow complexity, based on Shannon’s Mutual Information, named Interlacing Complexity Index (ICI). ICI is an objective quantification of the flow complexity from aneurysm inlet to aneurysm outlets. It measures how unpredictable is the location of the streamlines at the outlets from knowing the location at the inlet, relative to the scale of observation. We selected from the @neurIST database a set of 49 cerebral vasculatures with aneurysms in the middle cerebral artery. Surface models of patient-specific vascular geometries were obtained by geodesic active region segmentation and manual correction, and unsteady flow simulations were performed imposing physiological flow boundary conditions. The obtained ICI has been compared to several qualitative classifications performed by an expert, revealing high correlations

Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms

Image registration is an essential technique to obtain point correspondences between anatomical structures from different images. Conventional non-rigid registration methods assume a continuous and smooth deformation field throughout the image. However, the deformation field at the interface of different organs is not necessarily continuous, since the organs may slide over or separate from each other. Therefore, imposing continuity and smoothness ubiquitously would lead to artifacts and increased errors near the discontinuity interface. In computational mechanics, the eXtended Finite Element Method (XFEM) was introduced to handle discontinuities without using computational meshes that conform to the discontinuity geometry. Instead, the interpolation bases themselves were enriched with discontinuous functional terms. Borrowing this concept, we propose a multiresolution eXtented Free-Form Deformation (XFFD) framework that seamlessly integrates within and extends the standard Free-Form Deformation (FFD) approach. Discontinuities are incorporated by enriching the B-spline basis functions coupled with extra degrees of freedom, which are only introduced near the discontinuity interface. In contrast with most previous methods, restricted to sliding motion, no ad hoc penalties or constraints are introduced to reduce gaps and overlaps. This allows XFFD to describe more general discontinuous motions. In addition, we integrate XFFD into a rigorously formulated multiresolution framework by introducing an exact parameter upsampling method. The proposed method has been evaluated in two publicly available datasets: 4D pulmonary CT images from the DIR-Lab dataset and 4D CT liver datasets. The XFFD achieved a Target Registration Error (TRE) of 1.17 ± 0.85 mm in the DIR-lab dataset and 1.94 ± 1.01 mm in the liver dataset, which significantly improves on the performance of the state-of-the-art methods handling discontinuities

Population-based Bayesian regularization for microstructural diffusion MRI with NODDIDA.

PURPOSE:Information on the brain microstructure can be probed by Diffusion Magnetic Resonance Imaging (dMRI). Neurite Orientation Dispersion and Density Imaging with Diffusivities Assessment (NODDIDA) is one of the simplest microstructural model proposed. However, the estimation of the NODDIDA parameters from clinically plausible dMRI acquisition is ill-posed, and different parameter sets can describe the same measurements equally well. A few approaches to resolve this problem focused on developing better optimization strategies for this non-convex optimization. However, this fundamentally does not resolve ill-posedness. This article introduces a Bayesian estimation framework, which is regularized through knowledge from an extensive dMRI measurement set on a population of healthy adults (henceforth population-based prior). METHODS:We reformulate the problem as a Bayesian maximum a posteriori estimation, which includes as a special case previous approach using non-informative uniform priors. A population-based prior is estimated from 35 subjects of the MGH Adult Diffusion data (Human Connectome Project), acquired with an extensive acquisition protocol including high b-values. The accuracy and robustness of different approaches with and without the population-based prior is tested on subsets of the MGH dataset, and an independent dataset from a clinically comparable scanner, with only clinically plausible dMRI measurements. RESULTS:The population-based prior produced substantially more accurate and robust parameter estimates, compared to the conventional uniform priors, for clinically feasible protocols, without introducing any evident bias. CONCLUSIONS:The use of the proposed Bayesian population-based prior can lead to clinically feasible and robust estimation of NODDIDA parameters without changing the acquisition protocol

Quantifying Pelvic Periprosthetic Bone Remodeling Using Dual-Energy X-Ray Absorptiometry Region-Free Analysis

The gold standard tool for measuring periprosthetic bone mineral density (BMD) is dual-energy X-ray absorptiometry (DXA). However, resolution of the method is limited due to the aggregation of pixel data into large regions of interest for clinical and statistical analysis. We have previously validated a region-free analysis method (DXA-RFA) for quantitating BMD change at the pixel level around femoral prostheses. Here, we applied the DXA-RFA method to the pelvis, and quantitated its precision in this setting using repeated DXA scans taken on the same day after repositioning in 29 patients after total hip arthroplasty. Scans were semiautomatically segmented using edge detection, intensity thresholding, and morphologic operations, and elastically registered to a common template generated through generalized Procrustes analysis. Pixel-wise BMD precision between repeated scans was expressed as a coefficient of variation %. Longitudinal BMD change was assessed in an independent group of 24 patients followed up for 260 wk. DXA-RFA spatial resolution of 0.31 mm2 provided approximately 12,500 data points per scan. The median data-point precision was 17.8% (interquartile range 14.3%–22.7%). The anatomic distribution of the precision errors showed poorer precision at the bone borders and superior precision to the obturator foramen. Evaluation of longitudinal BMD showed focal BMD change at 260 wk of −26.8% adjacent to the prosthesis-bone interface (1% of bone map area). In contrast, BMD change of +39.0% was observed at the outer aspect of the ischium (3% of bone map area). Pelvic DXA-RFA is less precise than conventional DXA analysis. However, it is sensitive for detecting local BMD change events in groups of patients, and provides a novel tool for quantitating local bone mass after joint replacement. Using this method, we were able to resolve BMD change over small areas adjacent to the implant-bone interface and in the ischial region over 260 wk after total hip arthroplasty

A Spatio-Temporal Ageing Atlas of the Proximal Femur

Osteoporosis is an age-associated disease characterised by low bone mineral density (BMD) and micro-architectural deterioration leading to enhanced fracture risk. Conventional dual-energy X-ray absorptiometry (DXA) analysis has facilitated our understanding of BMD reduction in specific regions of interest (ROIs) within the femur, but cannot resolve spatial BMD patterns nor reflect age-related changes in bone microarchitecture due to its inherent averaging of pixel BMD values into large ROIs. To address these limitations and develop a comprehensive model of involutional bone loss, this paper presents a fully automatic pipeline to build a spatio-temporal atlas of ageing bone in the proximal femur. A new technique, termed DXA region free analysis (DXA RFA), is proposed to eliminate morphological variation between DXA scans by warping each image into a reference template. To construct the atlas, we use unprocessed DXA data from Caucasian women aged 20-97 years participating in three cohort studies in Western Europe (n > 13 ,000). A novel calibration procedure, termed quantile matching regression, is proposed to integrate data from different DXA manufacturers. Pixel-wise BMD evolution with ageing was modelled using smooth quantile curves. This technique enables characterisation of spatially-complex BMD change patterns with ageing, visualised using heat-maps. Furthermore, quantile curves plotted at different pixel coordinates showed consistently different rates of bone loss at different regions within the femoral neck. Given the close relationship between spatio-temporal bone loss and osteoporotic fracture, improved understanding of the bone ageing process could lead to enhanced prognostic, preventive and therapeutic strategies for the disease

Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding

Purpose: Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, it has been shown recently that, in the general Standard Model, parameter estimation from dMRI data is ill‐conditioned even when very high b‐values are applied. We analyze this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from single diffusion encoding (SDE) to double diffusion encoding (DDE) resolves the ill‐posedness for intermediate diffusion weightings, producing an increase in accuracy and precision of the parameter estimation. Methods: We analyze theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform in silico experiments to compare SDE and DDE capabilities under similar noise conditions. Results: We prove analytically that DDE provides invariant information non‐accessible from SDE, which makes the NODDIDA parameter estimation injective. The in silico experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space. Conclusions: DDE adds additional information for estimating the model parameters, unexplored by SDE. We show, as an example, that this is sufficient to solve the previously reported degeneracies in the NODDIDA model parameter estimation

Mixture of Probabilistic Principal Component Analyzers for Shapes from Point Sets

Inferring a probability density function (pdf) for shape from a population of point sets is a challenging problem. The lack of point-to-point correspondences and the non-linearity of the shape spaces undermine the linear models. Methods based on manifolds model the shape variations naturally, however, statistics are often limited to a single geodesic mean and an arbitrary number of variation modes. We relax the manifold assumption and consider a piece-wise linear form, implementing a mixture of distinctive shape classes. The pdf for point sets is defined hierarchically, modeling a mixture of Probabilistic Principal Component Analyzers (PPCA) in higher dimension. A Variational Bayesian approach is designed for unsupervised learning of the posteriors of point set labels, local variation modes, and point correspondences. By maximizing the model evidence, the numbers of clusters, modes of variations, and points on the mean models are automatically selected. Using the predictive distribution, we project a test shape to the spaces spanned by the local PPCA's. The method is applied to point sets from: i) synthetic data, ii) healthy versus pathological heart morphologies, and iii) lumbar vertebrae. The proposed method selects models with expected numbers of clusters and variation modes, achieving lower generalization-specificity errors compared to state-of-the-art