A SR-NET 3D-TO-2D ARCHITECTURE FOR PARASEPTAL EMPHYSEMA SEGMENTATION.

Paraseptal emphysema (PSE) is a relatively unexplored emphysema subtype that is usually asymptomatic, but recently associated with interstitial lung abnormalities which are related with clinical outcomes, including mortality. Previous local-based methods for emphysema subtype quantification do not properly characterize PSE. This is in part for their inability to properly capture the global aspect of the disease, as some the PSE lesions can involved large regions along the chest wall. It is our assumption, that path-based approaches are not well-suited to identify this subtype and segmentation is a better paradigm. In this work we propose and introduce the Slice-Recovery network (SR-Net) that leverages 3D contextual information for 2D segmentation of PSE lesions in CT images. For that purpose, a novel convolutional network architecture is presented, which follows an encoding-decoding path that processes a 3D volume to generate a 2D segmentation map. The dataset used for training and testing the method comprised 664 images, coming from 111 CT scans. The results demonstrate the benefit of the proposed approach which incorporate 3D context information to the network and the ability of the proposed method to identify and segment PSE lesions with different sizes even in the presence of other emphysema subtypes in an advanced stage.

EMPHYSEMA CLASSIFICATION USING A MULTI-VIEW CONVOLUTIONAL NETWORK.

In this article we propose and validate a fully automatic tool for emphysema classification in Computed Tomography (CT) images. We hypothesize that a relatively simple Convolutional Neural Network (CNN) architecture can learn even better discriminative features from the input data compared with more complex and deeper architectures. The proposed architecture is comprised of only 4 convolutional and 3 pooling layers, where the input corresponds to a 2.5D multiview representation of the pulmonary segment tissue to classify, corresponding to axial, sagittal and coronal views. The proposed architecture is compared to similar 2D CNN and 3D CNN, and to more complex architectures which involve a larger number of parameters (up to six times larger). This method has been evaluated in 1553 tissue samples, and achieves an overall sensitivity of 81.78 % and a specificity of 97.34%, and results show that the proposed method outperforms deeper state-of-the-art architectures particularly designed for lung pattern classification. The method shows satisfactory results in full-lung classification.

Image analysis for understanding embryo development: a bridge from microscopy to biological insights.

The digital reconstruction of the embryogenesis of model organisms from 3D+time data is revolutionizing practices in quantitative and integrative Developmental Biology. A manual and fully supervised image analysis of the massive complex data acquired with new microscopy technologies is no longer an option and automated image processing methods are required to fully exploit the potential of imaging data for biological insights. Current developments and challenges in biological image processing include algorithms for microscopy multiview fusion, cell nucleus tracking for quasi-perfect lineage reconstruction, segmentation, and validation methodologies for cell membrane shape identification, single cell gene expression quantification from in situ hybridization data, and multidimensional image registration algorithms for the construction of prototypic models. These tools will be essential to ultimately produce the multilevel in toto reconstruction that combines the cell lineage tree, cells, and tissues structural information and quantitative gene expression data in its spatio-temporal context throughout development.

Motion estimation of tagged cardiac magnetic resonance images using variational techniques.

This work presents a new method for motion estimation of tagged cardiac magnetic resonance sequences based on variational techniques. The variational method has been improved by adding a new term in the optical flow equation that incorporates tracking points with high stability of phase. Results were obtained through simulated and real data, and were validated by manual tracking and with respect to a reference state-of-the-art method: harmonic phase imaging (HARP). The error, measured in pixels per frame, obtained with the proposed variational method is one order of magnitude smaller than the one achieved by the reference method, and it requires a lower computational cost.

An automatic quantification and registration strategy to create a gene expression atlas of zebrafish embryogenesis.

In order to properly understand and model the gene regulatory networks in animals development, it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains. In this paper, we propose a complete computational framework to fulfill this task and create a 3D Atlas of the early zebrafish embryogenesis annotated with both the cellular localizations and the level of expression of different genes at different developmental stages. The strategy to construct such an Atlas is described here with the expression pattern of 5 different genes at 6 hours of development post fertilization.

List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations.

Respiratory motion in emission tomography leads to reduced image quality. Developed correction methodology has been concentrating on the use of respiratory synchronized acquisitions leading to gated frames. Such frames, however, are of low signal-to-noise ratio as a result of containing reduced statistics. In this work, we describe the implementation of an elastic transformation within a list-mode-based reconstruction for the correction of respiratory motion over the thorax, allowing the use of all data available throughout a respiratory motion average acquisition. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List-mode-data-based PET-simulated frames were subsequently produced by combining the NCAT datasets with Monte Carlo simulation. A non-rigid registration algorithm based on B-spline basis functions was employed to derive transformation parameters accounting for the respiratory motion using the NCAT dynamic CT images. The displacement matrices derived were subsequently applied during the image reconstruction of the original emission list mode data. Two different implementations for the incorporation of the elastic transformations within the one-pass list mode EM (OPL-EM) algorithm were developed and evaluated. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lung fields, the elastic-transformation-based correction leads to a more uniform improvement across the lungs for different lesion sizes and locations.

Cardiac motion analysis from ultrasound sequences using nonrigid registration: validation against Doppler tissue velocity.

Early detection of cardiac motion abnormalities is one of the main goals of quantitative cardiac image processing. This article presents a new method to compute the 2-D myocardial motion parameters from gray-scale 2-D echocardiographic sequences, making special emphasis on the validation of the proposed technique in comparison with Doppler tissue imaging. Myocardial motion is computed using a frame-to-frame nonrigid registration technique on the whole sequence. The key feature of our method is the use of an analytical representation of the myocardial displacement based on a semilocal parametric model of the deformation using Bsplines. Myocardial motion analysis is performed to obtain displacement, velocity and strain parameters. Robustness and speed are achieved by introducing a multiresolution optimization strategy. To validate the method, velocity measurements in three different regions-of-interest in the septum have been compared with those obtained with Doppler tissue velocity in healthy and pathologic subjects. Regression and Bland-Altman analysis show very good agreement between the two different approaches, with the great advantage that the new method overcomes the angle-dependency limitations of the Doppler techniques, providing both longitudinal and radial measurements.