Catálogo de publicaciones - libros

Liver metastases are an advanced stage of several types of cancer, usually treated with surgery. Intra-operative localization of these lesions is currently facilitated by intra-operative ultrasound (IOUS) and palpation, yielding a high rate of false positives due to benign abnormal regions. In this paper we present the integration of functional nuclear information from a gamma probe with IOUS, to provide a synchronized, real-time visualization that facilitates the detection of active metastases intra-operatively. We evaluate the system in an ex-vivo setup employing a group of physicians and medical technicians and show that the addition of functional imaging improves the accuracy of localizing and identifying malignant and benign lesions significantly. Furthermore we are able to demonstrate that the inclusion of an advanced, augmented visualization provides more reliability and confidence on classifying these lesions in the presented evaluation setup.

A fully automated method for quantitative analysis of dynamic contrast-enhanced MRI data acquired with low and high field scanners, using spin echo and gradient echo sequences, depicting various joints is presented. The method incorporates efficient pre-processing techniques and a robust algorithm for quantitative assessment of dynamic signal intensity vs. time curves. It provides differentiated information to the reader regarding areas with the most active perfusion and permits depiction of different disease activity in separate compartments of a joint. Additionally, it provides information on the speed of contrast agent uptake by various tissues. The method delivers objective and easily reproducible results, which have been favourably viewed by a number of medical experts.

Monitoring expertise development in surgery is likely to benefit from evaluations of cortical brain function. Brain behaviour is dynamic and nonlinear. The aim of this paper is to evaluate the application of a nonlinear dimensionality reduction technique to enhance visualisation of multidimensional functional Near Infrared Spectroscopy (fNIRS) data. Manifold embedding is applied to prefrontal haemodynamic signals obtained during a surgical knot tying task from a group of 62 healthy subjects with varying surgical expertise. The proposed method makes no assumption about the functionality of the data set and is shown to be capable of recovering the intrinsic low dimensional structure of brain data. After manifold embedding, Earth Mover’s Distance (EMD) is used to quantify different patterns of cortical behaviour associated with surgical expertise and analyse the degree of inter-hemispheric channel pair symmetry.

Magnetic Resonance Spectroscopy (MRS) along with MRI has emerged as a promising tool in diagnosis and potentially screening for prostate cancer. Surprisingly little work, however, has been done in the area of automated quantitative analysis of MRS data for identifying likely cancerous areas in the prostate. In this paper we present a novel approach that integrates a manifold learning scheme (spectral clustering) with an unsupervised hierarchical clustering algorithm to identify spectra corresponding to cancer on prostate MRS. Ground truth location for cancer on prostate was determined from the sextant location and maximum size of cancer available from the ACRIN database, from where a total of 14 MRS studies were obtained. The high dimensional information in the MR spectra is non linearly transformed to a low dimensional embedding space and via repeated clustering of the voxels in this space, non informative spectra are eliminated and only informative spectra retained. Our scheme successfully identified MRS cancer voxels with sensitivity of 77.8%, false positive rate of 28.92%, and false negative rate of 20.88% on a total of 14 prostate MRS studies. Qualitative results seem to suggest that our method has higher specificity compared to a popular scheme, -score, routinely used for analysis of MRS data.

Motivated by the current limitations of automated quantitative image analysis in discriminating among intracellular immunohistochemical (IHC) staining patterns, this paper presents a two-fold approach for IHC characterization that utilizes both the protein stain information and the surrounding tissue architecture. Through the use of a color unmixing algorithm, stained tissue sections are automatically decomposed into the IHC stain, which visualizes the target protein, and the counterstain which provides an objective indication of the underlying histologic architecture. Feature measures are subsequently extracted from both staining planes. In order to characterize the IHC expression pattern, this approach exploits the use of a non-traditional feature based on textons. Novel biologically motivated filter banks are introduced in order to derive texture signatures for different IHC staining patterns. Systematic experiments using this approach were used to classify breast cancer tissue microarrays which had been previously prepared using immuno-targeted nuclear, cytoplasmic, and membrane stains.

Automated visual-tracking of cell populations using phase contrast time-lapse microscopy is vital for quantitative, systematic and high-throughput measurements of cell behaviors. These measurements include the spatiotemporal quantification of migration, mitosis, apoptosis, and cell lineage. This paper presents an automated cell tracking system that can simultaneously track and analyze thousands of cells. The system performs tracking by cycling through frame-by-frame track compilation and spatiotemporal track linking, combining the power of two tracking paradigms. We applied the system to a range of cell populations including adult stem cells. The system achieved tracking accuracies in the range of 83.8%–92.5%, outperforming previous work by up to 8%.

We present a unified method, based on symmetric diffeomorphisms, for studying longitudinal neurodegeneration. Our method first uses symmetric diffeomorphic normalization to find a spatiotemporal parameterization of an individual’s image time series. The second step involves mapping a representative image or set of images from the time series into an optimal template space. The template mapping is then combined with the intrasubject spatiotemporal map to enable pairwise statistical tests to be performed on a population of normalized time series images. Here, we apply this longitudinal analysis protocol to study the gray matter atrophy patterns induced by frontotemporal dementia (FTD). We sample our normalized spatiotemporal maps at baseline (time zero) and time one year to generate an (AAM) that estimates the annual effect of FTD. This spatiotemporal normalization enables us to locate neuroanatomical regions that consistently undergo significant annual gray matter atrophy across the population. We found the majority of annual atrophy to occur in the frontal and temporal lobes in our population of 20 subjects. We also found significant effects in the hippocampus, insula and cingulate gyrus. Our novel results, significant at  < 0.05 after false discovery rate correction, are represented in local template space but also assigned Talairach coordinates and Brodmann and Anatomical Automatic Labeling (AAL) labels. This paper shows the statistical power of symmetric diffeomorphic normalization for performing deformation-based studies of longitudinal atrophy.

Deformation morphometry provides a sensitive approach to detecting and mapping subtle volume changes in the brain. Population based analyses of this data have been used successfully to detect characteristic changes in different neurodegenerative conditions. However, most studies have been limited to statistical mapping of the scalar volume change at each point in the brain, by evaluating the determinant of the Jacobian of the deformation field. In this paper we describe an approach to spatial normalisation and analysis of the full deformation tensor. The approach employs a spatial relocation and reorientation of tensors of each subject. Using the assumption of small changes, we use a linear modeling of effects of clinical variables on each deformation tensor component across a population. We illustrate the use of this approach by examining the pattern of significance and orientation of the volume change effects in recovery from alcohol abuse. Results show new local structure which was not apparent in the analysis of scalar volume changes.

We propose a non-parametric diffeomorphic image registration algorithm based on Thirion’s demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of free form deformations by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the true ones in terms of Jacobians.

The creation of 2D ultrasound mosaics is becoming a common clinical practice with a high clinical value. The next step coming along with the increasing availability of 2D array transducers is the creation of 3D mosaics. In the literature of ultrasound registration, the alignment of multiple images has not yet been addressed. Therefore, we propose registration strategies, which are able to cope with problems arising by multiple image alignment. Among others, we use which urges the usage of . In this paper, we propose alternative multivariate extensions based on a maximum likelihood framework. Experimental results show the good performance of the proposed registration strategies and similarity measures.