Angélica Atehortúa

Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis

PURPOSE Accurate measurement of the right ventricle (RV) volume is important for the assessment of the ventricular function and a biomarker of the progression of any cardiovascular disease. However, the high RV variability makes difficult a proper delineation of the myocardium wall. This paper introduces a new automatic method for segmenting the RV volume from short axis cardiac magnetic resonance (MR) images by a salient analysis of temporal and spatial observations. METHODS The RV volume estimation starts by localizing the heart as the region with the most coherent motion during the cardiac cycle. Afterward, the ventricular chambers are identified at the basal level using the isodata algorithm, the right ventricle extracted, and its centroid computed. A series of radial intensity profiles, traced from this centroid, is used to search a salient intensity pattern that models the inner-outer myocardium boundary. This process is iteratively applied toward the apex, using the segmentation of the previous slice as a regularizer. The consecutive 2D segmentations are added together to obtain the final RV endocardium volume that serves to estimate also the epicardium. RESULTS Experiments performed with a public dataset, provided by the RV segmentation challenge in cardiac MRI, demonstrated that this method is highly competitive with respect to the state of the art, obtaining a Dice score of 0.87, and a Hausdorff distance of 7.26 mm while a whole volume was segmented in about 3 s. CONCLUSIONS The proposed method provides an useful delineation of the RV shape using only the spatial and temporal information of the cine MR images. This methodology may be used by the expert to achieve cardiac indicators of the right ventricle function.

Automatic segmentation of 4D cardiac MR images for extraction of ventricular chambers using a spatio-temporal approach

An accurate ventricular function quantification is important to support evaluation, diagnosis and prognosis of several cardiac pathologies. However, expert heart delineation, specifically for the right ventricle, is a time consuming task with high inter-and-intra observer variability. A fully automatic 3D+time heart segmentation framework is herein proposed for short-axis-cardiac MRI sequences. This approach estimates the heart using exclusively information from the sequence itself without tuning any parameters. The proposed framework uses a coarse-to-fine approach, which starts by localizing the heart via spatio-temporal analysis, followed by a segmentation of the basal heart that is then propagated to the apex by using a non-rigid-registration strategy. The obtained volume is then refined by estimating the ventricular muscle by locally searching a prior endocardium- pericardium intensity pattern. The proposed framework was applied to 48 patients datasets supplied by the organizers of the MICCAI 2012 Right Ventricle segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.

Modelo para el análisis de la mortalidad en Colombia 2000-2012

OBJECTIVE To propose and evaluate a model for fitting and forecasting the mortality rates in Colombia that allows analyzing the trends by age, sex, region and cause of death. METHODOLOGY The national death registries were used as primary source of analysis. The data was pre-processed recodifying the cause of death and redistributing the garbage codes. The forecast model was formulated as a linear approximation with a set of variables of interest, in particular the population and gross domestic product (GDP) by region. RESULTS As study case we took the mortality under 5 years old, it decreased steadily since 2000 at the national level and at most of the regions. The predictive power of the proposed methodology was tested by fitting the model with the data from 2000 to 2011, the forecast for 2012 was compared with the actual rate, and these results show the model is reliable enough for most of the region-cause combinations. CONCLUSIONS The proposed methodology and model have the potential to become an instrument to guide health spending priorities using some kind of evidence.

Quantifying gait patterns in Parkinson's disease.

Parkinson’s disease (PD) is constituted by a set of motor symptoms, namely tremor, rigidity, and bradykinesia, which are usually described but not quantified. This work proposes an objective characterization of PD gait patterns by approximating the single stance phase a single grounded pendulum. This model estimates the force generated by the gait during the single support from gait data. This force describes the motion pattern for different stages of the disease. The model was validated using recorded videos of 8 young control subjects, 10 old control subjects and 10 subjects with Parkinson’s disease in different stages. The estimated force showed differences among stages of Parkinson disease, observing a decrease of the estimated force for the advanced stages of this illness.

Quantifying stimulus-response rehabilitation protocols by auditory feedback in Parkinson's disease gait pattern

External auditory cues stimulate motor related areas of the brain, activating motor ways parallel to the basal ganglia circuits and providing a temporary pattern for gait. In effect, patients may re-learn motor skills mediated by compensatory neuroplasticity mechanisms. However, long term functional gains are dependent on the nature of the pathology, follow-up is usually limited and reinforcement by healthcare professionals is crucial. Aiming to cope with these challenges, several researches and device implementations provide auditory or visual stimulation to improve Parkinsonian gait pattern, inside and outside clinical scenarios. The current work presents a semiautomated strategy for spatio-temporal feature extraction to study the relations between auditory temporal stimulation and spatiotemporal gait response. A protocol for auditory stimulation was built to evaluate the integrability of the strategy in the clinic practice. The method was evaluated in transversal measurement with an exploratory group of people with Parkinson’s (n = 12 in stage 1, 2 and 3) and control subjects (n =6). The result showed a strong linear relation between auditory stimulation and cadence response in control subjects (R=0.98 ±0.008) and PD subject in stage 2 (R=0.95 ±0.03) and stage 3 (R=0.89 ±0.05). Normalized step length showed a variable response between low and high gait velocity (0.2> R >0.97). The correlation between normalized mean velocity and stimulus was strong in all PD stage 2 (R>0.96) PD stage 3 (R>0.84) and controls (R>0.91) for all experimental conditions. Among participants, the largest variation from baseline was found in PD subject in stage 3 (53.61 ±39.2 step/min, 0.12 ± 0.06 in step length and 0.33 ± 0.16 in mean velocity). In this group these values were higher than the own baseline. These variations are related with direct effect of metronome frequency on cadence and velocity. The variation of step length involves different regulation strategies and could need others specific external cues. In conclusion the current protocol (and their selected parameters, kind of sound time for training, step of variation, range of variation) provide a suitable gait facilitation method specially for patients with the highest gait disturbance (stage 2 and 3). The method should be adjusted for initial stages and evaluated in a rehabilitation program.

Cerebral palsy characterization by estimating ocular motion

Cerebral palsy (CP) is a large group of motion and posture disorders caused during the fetal or infant brain development. Sensorial impairment is commonly found in children with CP, i.e., between 40-75 percent presents some form of vision problems or disabilities. An automatic characterization of the cerebral palsy is herein presented by estimating the ocular motion during a gaze pursuing task. Specifically, After automatically detecting the eye location, an optical flow algorithm tracks the eye motion following a pre-established visual assignment. Subsequently, the optical flow trajectories are characterized in the velocity-acceleration phase plane. Differences are quantified in a small set of patients between four to ten years.

Fusion of 4D echocardiography and cine cardiac magnetic resonance volumes using a salient spatio-temporal analysis

An accurate left (LV) and right ventricular (RV) function quantification is important to support evaluation, diagnosis and prognosis of cardiac pathologies such as the cardiomyopathies. Currently, diagnosis by ultrasound is the most cost-effective examination. However, this modality is highly noisy and operator dependent, hence prone to errors. Therefore, fusion with other cardiac modalities may provide complementary information and improve the analysis of the specific pathologies like cardiomyopathies. This paper proposes an automatic registration between two complementary modalities, 4D echocardiography and Magnetic resonance images, by mapping both modalities to a common space of salience where an optimal registration between them is estimated. The obtained matrix transformation is then applied to the MRI volume which is superimposed to the 4D echocardiography. Manually selected marks in both modalities are used to evaluate the precision of the superimposition. Preliminary results, in three evaluation cases, show the distance between these marked points and the estimated with the transformation is about 2 mm.

Quantifying the gait pattern adaptation to auditory feedback in healthy elder adults

Several approaches using auditory feedback have been proposed to improve gait rehabilitation in Parkinson Disease. Despite auditory cues have shown to be useful, there are still unanswered questions about their optimal usage regarding parameters like frequency, number of beats and their integration with rehabilitation protocols, among others. Most approaches have attempted to resolve these questions by measuring their direct effect on spatiotemporal gait variables. However, few studies have assessed how synchronized the auditory feedback and the gait pattern are. The main goal was to quantify synchronization between the gait temporal patterns and the auditory stimuli. The group of participants consisted of seven (7) healthy subjects, aged between 50-70 years (average 57.28, ± 5.87 years), with average height of 1.64±0.09m and independent community ambulation. Each candidate was asked to sign an informed consent, given their good cognitive conditions for understanding the nature and purpose of the study. Participants were instructed to follow the sounds provided by a metronome. Feet tracking yielded the temporal gait pattern. The temporal coherence metric was developed to evaluate synchronization between audio signal and subject motion, in terms of phase shift (π radian). Results show a good fit to auditory stimulus in metronome rates between 140-150 and 60-80 beats/min (bpm) for the selected participants. A lower temporal coherence was observed at the beginning and the end of the test. The proposed metric allows quantification of the temporal coherence between gait and auditory cues in healthy elder subjects. Other exploratory trials should be directed to evaluate the temporal coherence between auditory stimuli and generated movements in population with Parkinson Disease.

Registration of cardiac magnetic resonance images using SURF points and matching

Reconstruction of the heartbeat is an useful tool to detect and diagnose some pathologies. However, this process represents a challenge because the heart is a moving organ inside a moving body, so that, either similar regions are hard to identify or some regions appear and disappear constantly. This article presents a reconstruction method of the right ventricle using SURF points in irregular regions. The SURF points, invariant to image scale and rotation, provide robust features of a right ventricle slice that can then be traced to the other slices. By using such points and then, using a nonrigid registration, it possible to perform a volumetrical reconstruction of these images.

Quantifying Parkinson's disease progression by simulating gait patterns

Modern rehabilitation protocols of most neurodegenerative diseases, in particular the Parkinson Disease, rely on a clinical analysis of gait patterns. Currently, such analysis is highly dependent on both the examiner expertise and the type of evaluation. Development of evaluation methods with objective measures is then crucial. Physical models arise as a powerful alternative to quantify movement patterns and to emulate the progression and performance of specific treatments. This work introduces a novel quantification of the Parkinson disease progression using a physical model that accurately represents the main gait biomarker, the body Center of Gravity (CoG). The model tracks the whole gait cycle by a coupled double inverted pendulum that emulates the leg swinging for the single support phase and by a damper-spring System (SDP) that recreates both legs in contact with the ground for the double phase. The patterns generated by the proposed model are compared with actual ones learned from 24 subjects in stages 2,3, and 4. The evaluation performed demonstrates a better performance of the proposed model when compared with a baseline model(SP) composed of a coupled double pendulum and a mass-spring system. The Frechet distance measured differences between model estimations and real trajectories, showing for stages 2, 3 and 4 distances of 0.137, 0.155, 0.38 for the baseline and 0.07, 0.09, 0.29 for the proposed method.