Parsing volumetric computed tomography (CT) into 10 or more salient organs simultaneously is a challenging task with many applications such as personalized scan planning and dose reporting. In the clinic, pre-scan data can come in the form of very low dose volumes acquired just prior to the primary scan or from an existing primary scan. To localize organs in such diverse data, we propose a new learning based framework that we call hierarchical pictorial structures (HPS) which builds multiple levels of models in a tree-like hierarchy that mirrors the natural decomposition of human anatomy from gross structures to finer structures. Each node of our hierarchical model learns (1) the local appearance and shape of structures, and (2) a generative global model that learns probabilistic, structural arrangement. Our main contribution is twofold. First we embed the pictorial structures approach in a hierarchical framework which reduces test time image interpretation and allows for the incorporation of additional geometric constraints that robustly guide model fitting in the presence of noise. Second we guide our HPS framework with the probabilistic cost maps extracted using random decision forests using volumetric 3D HOG features which makes our model fast to train and fast to apply to novel test data and posses a high degree of invariance to shape distortion and imaging artifacts. All steps require approximate 3 mins to compute and all organs are located with suitably high accuracy for our clinical applications such as personalized scan planning for radiation dose reduction. We assess our method using a database of volumetric CT scans from 81 subjects with widely varying age and pathology and with simulated ultra-low dose cadaver pre-scan data.
Spectral-domain optical coherence tomography (SD-OCT) finds widespread use clinically for the detection and management of ocular diseases. This non-invasive imaging modality has also begun to find frequent use in research studies involving animals such as mice. Numerous approaches have been proposed for the segmentation of retinal surfaces in SD-OCT images obtained from human subjects; however, the segmentation of retinal surfaces in mice scans is not as well-studied. In this work, we describe a graph-theoretic segmentation approach for the simultaneous segmentation of 10 retinal surfaces in SD-OCT scans of mice that incorporates learned shape priors. We compared the method to a baseline approach that did not incorporate learned shape priors and observed that the overall unsigned border position errors reduced from 3.58 +/- 1.33 μm to 3.20 +/- 0.56 μm.
This work addresses the challenging problem of parsing 2D radiographs into salient anatomical regions such as
the left and right lungs and the heart. We propose the integration of an automatic detection of a constellation
of landmarks via rejection cascade classifiers and a learned geometric constellation subset detector model with
a multi-object active appearance model (MO-AAM) initialized by the detected landmark constellation subset.
Our main contribution is twofold. First, we propose a recovery method for false positive and negative landmarks
which allows to handle extreme ranges of anatomical and pathological variability. Specifically we (1) recover
false negative (missing) landmarks through the consensus of inferences from subsets of the detected landmarks,
and (2) choose one from multiple false positives for the same landmark by learning Gaussian distributions for the
relative location of each landmark. Second, we train a MO-AAM using the true landmarks for the detectors and
during test, initialize the model using the detected landmarks. Our model fitting allows simultaneous localization
of multiple regions by encoding the shape and appearance information of multiple objects in a single model. The
integration of landmark detection method and MO-AAM reduces mean distance error of the detected landmarks
from 20.0mm to 12.6mm. We assess our method using a database of scout CT scans from 80 subjects with widely
varying pathology.
Four-dimensional CT scans provides valuable motion information of patient throughout different respiratory phases. PET, on the other hand, provides functional information about tumor, which differentiate tumor from normal tissue effectively. However, manually contouring structures of interest on 4D CT is prohibitively tedious due to the large amount of data. In this paper, we propose an automatic method to segment lung tumor simultaneously for 4D CT scans in all phases and PET scan. The problem is modeled as an optimization problem based on Markov Random Fields (MRF) which involves region, boundary terms and a regularization term between PET and CT scans. The problem is solved optimally by computing a single max flow in a properly constructed graph. As far as the authors know, this is the first work in simultaneously segmenting tumor in 4D CT while incorporating PET information. Experiments on 3 lung cancer patients are conducted. The average Dice coefficient is improved from 0.680 to 0.791 compared to segmenting tumor volume in 4D CT phase by phase without incorporating PET information. The proposed method is efficient in terms of running time since the method only requires computing a max flow for which efficient algorithm exists. The memory consumption is linearly scalable with respect to number of 4D CT phases, which enables our method to handle multiple 4D CT phases with reasonable memory consumption.
Despite extensive studies in the past, the problem of segmenting globally optimal multiple
surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even
harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical
iterative method based on a volumetric graph representation of the 3D image that incorporates
curvature and shape prior information. Compared with the graph-based method, applying the shape prior
to construct the graph on a specific preferred shape model allows easy incorporation of a wide spectrum
of shape prior information. Furthermore, the key insight that computation of the objective function can
be done independently in the x and y directions makes local improvement possible. Thus, instead of using
global optimization technique such as maximum flow algorithm, the iteration based method is much faster.
Additionally, the utilization of the curvature in the objective function ensures the smoothness. To the best
of our knowledge, this is the first paper to combine the shape-prior penalties with utilizing curvature in
objective function to ensure the smoothness of the generated surfaces while striving for achieving global
optimality. To evaluate the performance of our method, we test it on a set of 14 3D OCT images. Comparing
to the best existing approaches, our experiments suggest that the proposed method reduces the unsigned
surface positioning errors form 5.44 ± 1.07(μm) to 4.52 ± 0.84(μm). Moreover, our method has a much
improved running time, yields almost the same global optimality but with much better smoothness, which
makes it especially suitable for segmenting highly noisy images. The proposed method is also suitable for
parallel implementation on GPUs, which could potentially allow us to segment highly noisy volumetric
images in real time.
KEYWORDS: Image segmentation, 3D image processing, Magnetic resonance imaging, Image processing algorithms and systems, 3D modeling, Medical imaging, Detection and tracking algorithms, Data modeling, Feature extraction, 3D vision
We present a novel method for incorporating both edge and regional image information in a 3-D graph-theoretic
approach for globally optimal surface segmentation. The energy functional takes a ratio form of the "onsurface"
cost and the "in-region" cost. We thus introduce an optimal surface segmentation model allowing
regional information such as volume, homogeneity and texture to be included with boundary information such
as intensity gradients. Compared to the linear combination as in the standard active contour energies, this ratioform
energy is parameter free with no bias toward either a large or small region. Our method is the first attempt
to use a ratio-form energy functional in graph search framework for high dimensional image segmentation, which
delivers a globally optimal solution in polynomial time. The globally optimal surface can be achieved by solving
a parametric maximum flow problem in the time complexity of computing a single maximum flow. Our new
approach is applied to the aorta segmentation of 15 3-D MR aortic images from 15 subjects.
Compared to an expert-defined independent standard, the overall mean unsigned surface positioning error
was 0.76± 0.88 voxels. Our experiments showed that the incorporation of the regional information was effective
to alleviate the interference of adjacent objects.
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