5 research outputs found
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge
Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1
Enlargement, subdivision and individualization of statistical shape models: Application to 3D medical image segmentation
This thesis presents three original and complementary approaches to enhance the quality
of Statistical Shape Models (SSMs), that improve the accuracy of medical image
segmentation in challenging applications. First, we enhance the statistical richness of SSMs
by developing a technique capable of merging the shape representations and statistical
properties of several pre-existing models with no original or additional raw data. Second,
we enhance the geometrical quality of SSMs by developing a framework for modeling
simultaneously both global and local characteristics of highly complex and/or multi-part
anatomical shapes. Last, we improve the specificity of SSMs for specific subjects by
integrating individual-specific non-imaging metadata such as demographic, clinical and
behavioral variables into the SSM construction and image segmentation tasks. These
techniques are demonstrated and validated by considering various imaging modalities such
as magnetic resonance imaging (MRI) and computed tomography (CT), and different
complex anatomies, including the human heart, brain and spine.Esta tesis presenta tres propuestas originales y complementarias para mejorar la
calidad de los modelos estadÃsticos de formas (SSMs) que mejoran la precisión
de la segmentación de la imagen médica en aplicaciones difÃciles. Proponemos,
primero, mejorar la riqueza estadÃstica de los SSMs por medio de una técnica para
unir la representación de forma y las propiedades estadÃsticas de muchos modelos
pre-existentes sin observaciones adicionales. Segundo, mejorar la representacion
geométrica de los SSMs modelando simultáneamente las caracterÃsticas globales
y locales del objecto o de multiples anatomias. Por último, mejorar la
especificidad de los SSMs mediante la integración de metadatos del paciente no
derivados de la imagen, tales como, variables demográficas, conductuales y de
entorno clÃnico, en la construcción de los modelos. Estas técnicas son
demostradas y validadas en imágenes de resonancia magnética (MRI) y
tomografÃa computarizada (CT) y en anatomias como el corazón, el cerebro y la
espina dorsal humanos
Statistical shape modeling using partial least squares:Application to the assessment of myocardial infarction
A framework for the merging of pre-existing and correspondenceless 3D statistical shape models
Automatic Assessment of Full LV Coverage in Cardiac Cine MRI Using Learned Intensity Attributes with Discriminative 3D CNNs
<div>This dataset has been created to serve as complimentary material to a journal publication currently under review by IEEE Transactions on Medical Imaging. </div><div><br></div><div>The dataset contains two files, a comma-separated file (.csv) and a video (.avi). The first file contains the results of applying our Discriminative 3D CNN (D3D CNN) Image Quality Assessment (IQA) algorithm to a set of images from the UK Biobank dataset. The Image Quality Assessment file named "IQA spreadsheet.csv" contains 3 columns. The first column labeled "f.eid" lists 5000+ patient IDs, the next column labeled "MBS" (Missing Basal Slice) contains binary values indicating the presence or absence of the basal slice. Similarly the last column labeled "MAS" (Missing Apical Slice) indicates the presence or absence of the apical slice in the image volume. </div><div><br></div><div>The second file is a demo of the algorithm's functionality. The video shows a unique patient's cardiac image volume, and the result of the detection/non-detection of the basal and apical slices indicated with green and red labels on the images and on a side panel. </div><div><br></div