Maria J. Ledesma-Carbayo

Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness. We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method.

A Benchmark for Comparison of Cell Tracking Algorithms

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge. Contact: codesolorzano@unav.es Supplementary information: Supplementary data are available at Bioinformatics online.

Noninvasive Identification of Ventricular Tachycardia-Related Conducting Channels Using Contrast-Enhanced Magnetic Resonance Imaging in Patients With Chronic Myocardial Infarction Comparison of Signal Intensity Scar Mapping and Endocardial Voltage Mapping

OBJECTIVES We performed noninvasive identification of post-infarction sustained monomorphic ventricular tachycardia (SMVT)-related slow conduction channels (CC) by contrast-enhanced magnetic resonance imaging (ceMRI). BACKGROUND Conduction channels identified by voltage mapping are the critical isthmuses of most SMVT. We hypothesized that CC are formed by heterogeneous tissue (HT) within the scar that can be detected by ceMRI. METHODS We studied 18 consecutive VT patients (SMVT group) and 18 patients matched for age, sex, infarct location, and left ventricular ejection fraction (control group). We used ceMRI to quantify the infarct size and differentiate it into scar core and HT based on signal-intensity (SI) thresholds (>3 SD and 2 to 3 SD greater than remote normal myocardium, respectively). Consecutive left ventricle slices were analyzed to determine the presence of continuous corridors of HT (channels) in the scar. In the SMVT group, color-coded shells displaying ceMRI subendocardial SI were generated (3-dimensional SI mapping) and compared with endocardial voltage maps. RESULTS No differences were observed between the 2 groups in myocardial, necrotic, or heterogeneous mass. The HT channels were more frequently observed in the SMVT group (88%) than in the control group (33%, p < 0.001). In the SMVT group, voltage mapping identified 26 CC in 17 of 18 patients. All CC corresponded, in location and orientation, to a similar channel detected by 3-dimensional SI mapping; 15 CC were related to 15 VT critical isthmuses. CONCLUSIONS SMVT substrate can be identified by ceMRI scar heterogeneity analysis. This information could help identify patients at risk of VT and facilitate VT ablation.

Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses.

We have applied automated image analysis methods in the assessment of human kidney perfusion based on 3D dynamic contrast-enhanced MRI data. This approach consists of non-rigid 3D image registration of the moving kidney followed by k-means clustering of the voxel time courses with split between left and right kidney. This method was applied to four data sets acquired from healthy volunteers, using 1.5 T (2 exams) and 3 T scanners (2 exams). The proposed registration method reduced motion artifacts in the image time series and improved further analysis of the DCE-MRI data. The subsequent clustering to segment the kidney compartments was in agreement with manually delineations (similarity score of 0.96) in the same motion corrected images. The resulting mean intensity time curves clearly show the successive transition of contrast agent through kidney compartments (cortex, medulla, and pelvis). The proposed method for motion correction and kidney compartment segmentation might improve the validity and usefulness of further model-based pharmacokinetic analysis of kidney function in patients.

Cardiac Motion Analysis from Ultrasound Sequences Using Non-rigid Registration

In this article we propose a cardiac motion estimation technique that uses non-rigid registration to compute the dense cardiac displacement field from 2D ultrasound sequences. Our method employs a semi-local deformation model which provides controlled smoothness. We apply a multiresolution optimization strategy for better speed and robustness. To further improve the accuracy, the sequence is registered in both forward and backward directions. We calculate additional parameters from the displacement field, such as total displacement and strain.We create an artificial ultrasound sequence ofon e heart cycle using a motion model and use it to validate the accuracy oft he algorithm. Finally, we present results on real data from normal and pathological subjects that show the clinical applicability of our method.

Automated Processing of Zebrafish Imaging Data: A Survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time-frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32±12s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time-intensity curves from .84±.19 before registration to .96±.06 after registration.

Iterative Image Reconstruction for Clinical PET Using Ordered Subsets, Median Root Prior, and a Web-Based Interface

PURPOSE The development, implementation and validation of simple, flexible and efficient iterative image reconstruction (IIR) methods for their take-up in routine clinical positron emission tomography (PET) static or dynamic studies. PROCEDURES The ordered subsets (OS) technique applied for the acceleration of the maximum likelihood expectation maximization (MLEM) IIR algorithm is here extended to include the weighted least-squares (WLS), image space reconstruction algorithm (ISRA) and the space alternating generalized EM (SAGE). The median root prior (MRP) has been successfully applied as a Bayesian regularization to control the noise level in the reconstructed images. All methods are implemented on distributed Pentium systems and tested using simulated PET data from a brain phantom. A Javascript is used for the initiation of the reconstruction. RESULTS Taking into consideration the image quality and the time required for the reconstruction, the MRP-OSEM (ordered subsets expectation maximization) seems to provide best results after four to eight iterations, with four subsets and a MRP coefficient of 0.2-0.4. Iterative reconstruction of the transmission images with OS-acceleration and MRP regularization with subsequent calculation of the attenuation correction factors (ACFs) is shown to effectively remove streak artifacts in the emission images, especially along paths of high attenuation. CONCLUSIONS An efficient implementation using distributed processing principles and a web-based interface allows the reconstruction of one frame (with 63 cross-section slices) from a dynamic determination in few minutes. This work showed that regular PC systems can provide fast execution and produce results in clinically meaningful times. This eradicates the argument of the computational burden of the method that prevented the extensive use of IIR in todays modern PET systems.

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.