Albert Montillo

Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain

We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimers disease.

Tagged magnetic resonance imaging of the heart: a survey

Magnetic resonance imaging (MRI) of the heart with magnetization tagging provides a potentially useful new way to assess cardiac mechanical function, through revealing the local motion of otherwise indistinguishable portions of the heart wall. While still an evolving area, tagged cardiac MRI is already able to provide novel quantitative information on cardiac function. Exploiting this potential requires developing tailored methods for both imaging and image analysis. In this paper, we review some of the progress that has been made in developing such methods for tagged cardiac MRI, as well as some of the ways these methods have been applied to the study of cardiac function.

Entangled decision forests and their application for semantic segmentation of CT images

This work addresses the challenging problem of simultaneously segmenting multiple anatomical structures in highly varied CT scans. We propose the entangled decision forest (EDF) as a new discriminative classifier which augments the state of the art decision forest, resulting in higher prediction accuracy and shortened decision time. Our main contribution is two-fold. First, we propose entangling the binary tests applied at each tree node in the forest, such that the test result can depend on the result of tests applied earlier in the same tree and at image points offset from the voxel to be classified. This is demonstrated to improve accuracy and capture long-range semantic context. Second, during training, we propose injecting randomness in a guided way, in which node feature types and parameters are randomly drawn from a learned (nonuniform) distribution. This further improves classification accuracy. We assess our probabilistic anatomy segmentation technique using a labeled database of CT image volumes of 250 different patients from various scan protocols and scanner vendors. In each volume, 12 anatomical structures have been manually segmented. The database comprises highly varied body shapes and sizes, a wide array of pathologies, scan resolutions, and diverse contrast agents. Quantitative comparisons with state of the art algorithms demonstrate both superior test accuracy and computational efficiency.

Age regression from faces using random forests

Predicting the age of a person through face image analysis holds the potential to drive an extensive array of real world applications from human computer interaction and security to advertising and multimedia. In this paper the first application of the random forest for age regression is proposed. This method offers the advantage of few parameters that are relatively easy to initialize. Our method learns salient anthropometric quantities without a prior model. Significant implications include a dramatic reduction in training time while maintaining high regression accuracy throughout human development.

Automated Segmentation of the Left and Right Ventricles in 4D Cardiac SPAMM Images

In this paper we describe a completely automated volume-based method for the segmentation of the left and right ventricles in 4D tagged MR (SPAMM) images for quantitative cardiac analysis. We correct the background intensity variation in each volume caused by surface coils using a new scale-based fuzzy connectedness procedure. We apply 3D grayscale opening to the corrected data to create volumes containing only the blood filled regions. We threshold the volumes by minimizing region variance or by an adaptive statistical thresholding method. We isolate the ventricular blood filled regions using a novel approach based on spatial and temporal shape similarity. We use these regions to define the endocardium contours and use them to initialize an active contour that locates the epicardium through the gradient vector flow of an edgemap of a grayscale-closed image. Both quantitative and qualitative results on normal and diseased patients are presented.

Segmenting cardiac MRI tagging lines using Gabor filter banks

This paper describes a new method for the automated segmentation and extraction of cardiac MRI tagging lines. Our method is based on the novel use of a 2D Gabor filter bank. By convolving the tagged input image with our Gabor filters, the tagging lines are automatically enhanced and segmented out. We design the Gabor filter bank based on the input images spatial and frequency characteristics. The final result is a combination of each filters response in the Gabor filter bank. We demonstrate that compared to bandpass filter methods such as HARP, this method results in robust and accurate segmentation of the tagging lines.

Combining generative and discriminative models for semantic segmentation of CT scans via active learning

This paper presents a new supervised learning framework for the efficient recognition and segmentation of anatomical structures in 3D computed tomography (CT), with as little training data as possible. Training supervised classifiers to recognize organs within CT scans requires a large number of manually delineated exemplar 3D images, which are very expensive to obtain. In this study, we borrow ideas from the field of active learning to optimally select a minimum subset of such images that yields accurate anatomy segmentation. The main contribution of this work is in designing a combined generative-discriminative model which: i) drives optimal selection of training data; and ii) increases segmentation accuracy. The optimal training set is constructed by finding unlabeled scans which maximize the disagreement between our two complementary probabilistic models, as measured by a modified version of the Jensen-Shannon divergence. Our algorithm is assessed on a database of 196 labeled clinical CT scans with high variability in resolution, anatomy, pathologies, etc. Quantitative evaluation shows that, compared with randomly selecting the scans to annotate, our method decreases the number of training images by up to 45%. Moreover, our generative model of body shape substantially increases segmentation accuracy when compared to either using the discriminative model alone or a generic smoothness prior (e.g. via a Markov Random Field).

Extracting tissue deformation using Gabor filter banks

This paper presents a new approach for accurate extraction of tissue deformation imaged with tagged MR. Our method, based on banks of Gabor filters, adjusts (1) the aspect and (2) orientation of the filter’s envelope and adjusts (3) the radial frequency and (4) angle of the filter’s sinusoidal grating to extract information about the deformation of tissue. The method accurately extracts tag line spacing, orientation, displacement and effective contrast. Existing, non-adaptive methods often fail to recover useful displacement information in the proximity of tissue boundaries while our method works in the proximity of the boundaries. We also present an interpolation method to recover all tag information at a finer resolution than the filter bank parameters. Results are shown on simulated images of translating and contracting tissue.

Automated Model-Based Segmentation of the Left and Right Ventricles in Tagged Cardiac MRI

We describe an automated, model-based method to segment the left and right ventricles in 4D tagged MR. We fit 3D epicardial and endocardial surface models to ventricle features we extract from the image data. Excellent segmentation is achieved using novel methods that (1) initialize the models and (2) that compute 3D model forces from 2D tagged MR images. The 3D forces guide the models to patient-specific anatomy while the fit is regularized via internal deformation strain energy of a thin plate. Deformation continues until the forces equilibrate or vanish. Validation of the segmentations is performed quantitatively and qualitatively on normal and diseased subjects.

Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests

Accurate automated segmentation of brain tumors in MR images is challenging due to overlapping tissue intensity distributions and amorphous tumor shape. However, a clinically viable solution providing precise quantification of tumor and edema volume would enable better pre-operative planning, treatment monitoring and drug development. Our contributions are threefold. First, we design efficient gradient and LBPTOP based texture features which improve classification accuracy over standard intensity features. Second, we extend our texture and intensity features to symmetric texture and symmetric intensity which further improve the accuracy for all tissue classes. Third, we demonstrate further accuracy enhancement by extending our long range features from 100 mm to a full 200 mm. We assess our brain segmentation technique on 20 patients in the BraTS 2012 dataset. Impact from each contribution is measured and the combination of all the features is shown to yield state-of-the-art accuracy and speed.