This work involves the computer-aided diagnosis (CAD) of pulmonary embolism (PE) in contrast-enhanced computed
tomography pulmonary angiography (CTPA). Contrast plays an important role in analyzing and identifying PE in CTPA.
At times the contrast mixing in blood may be insufficient due to several factors such as scanning speed, body weight and
injection duration. This results in a suboptimal study (mixing artifact) due to non-homogeneous enhancement of blood's
opacity. Most current CAD systems are not optimized to detect PE in sub optimal studies. To this effect, we propose new
techniques for CAD to work robustly in both optimal and suboptimal situations.
First, the contrast level at the pulmonary trunk is automatically detected using a landmark detection tool. This
information is then used to dynamically configure the candidate generation (CG) and classification stages of the
algorithm. In CG, a fast method based on tobogganing is proposed which also detects wall-adhering emboli. In addition,
our proposed method correctly encapsulates potential PE candidates that enable accurate feature calculation over the
entire PE candidate. Finally a classifier gating scheme has been designed that automatically switches the appropriate
classifier for suboptimal and optimal studies.
The system performance has been validated on 86 real-world cases collected from different clinical sites. Results
show around 5% improvement in the detection of segmental PE and 6% improvement in lobar and sub segmental PE
with a 40% decrease in the average false positive rate when compared to a similar system without contrast detection.
We present an automatic method to quickly and accurately detect multiple anatomy region-of-interests (ROIs) from CT
topogram images. Our method first detects a redundant and potentially erroneous set of local features. Their spatial
configurations are captured by a set of local voting functions. Unlike all the existing methods where the idea was to try to
"hit" the correct/best constellations of local features, we have taken an opposite approach. We try to peel away the bad
features until a safe (i.e., conservatively small) number of features remain. It is deterministic in nature and guarantees
a success even for extremely noisy cases. The advantages of the method are its robustness and computational efficiency.
Our method also addresses the potential scenario in which outliers (i.e., false landmarks detections) forms plausible
configurations. As long as such outliers are a minority, the method can successfully remove these outliers. The final ROI
of the anatomy is computed from a best subset of the remaining local features. Experimental validation was carried out
for multiple organs detection from a large collection of CT topogram images. Fast and highly robust performance was
observed. In the testing data sets, the detection rate varies from 98.2% to 100% for different ROIs and the false detection
rate is from 0.0% to 0.5% for different ROIs. The method is fast and accurate enough to be seamlessly integrated into a
real-time work flow on the CT machine to improve efficiency, consistency, and repeatability.
Emerging whole-body imaging technologies push computer aided detection/diagnosis (CAD) to scale up to a
whole-body level, which involves multiple organs or anatomical structure. To be exploited in this paper is the
fact that the various tasks in whole-body CAD are often highly dependent (e.g., the localization of the femur
heads strongly predicts the position of the iliac bifurcation of the aorta). One way to effectively employ task
dependency is to schedule the tasks such that outputs of some tasks are used to guide the others. In this sense,
optimal task scheduling is key to improve overall performance of a whole-body CAD system. In this paper,
we propose a method for task scheduling that is optimal in an information-theoretic sense. The central idea
is to schedule tasks in such an order that each operation achieves maximum expected information gain over
all the tasks. The formulation embeds two intuitive principles: (1) a task with higher confidence tends to be
scheduled earlier; (2) a task with higher predictive power for other tasks tends to be scheduled earlier. More
specifically, task dependency is modeled by conditional probability; the outcome of each task is assumed to be
probabilistic as well; and the objective function is based on the reduction of the summed conditional entropy
over all tasks. The validation is carried out on a challenging CAD problem, multi-organ localization in whole-body
CT. Compared to unscheduled and ad hoc scheduled organ detection/localization, our scheduled execution
achieves higher accuracy with much less computation time.
Pulmonary embolism (PE) is a serious medical condition, characterized by the partial/complete blockage of an
artery within the lungs. We have previously developed a fast yet effective approach for computer aided detection
of PE in computed topographic pulmonary angiography (CTPA),1 which is capable of detecting both acute and
chronic PEs, achieving a benchmark performance of 78% sensitivity at 4 false positives (FPs) per volume. By
reviewing the FPs generated by this system, we found the most dominant type of FP, roughly one third of all
FPs, to be lymph/connective tissue. In this paper, we propose a novel approach that specifically aims at reducing
this FP type. Our idea is to explicitly exploit the anatomical context configuration of PE and lymph tissue in the
lungs: a lymph FP connects to the airway and is located outside the artery, while a true PE should not connect
to the airway and must be inside the artery. To realize this idea, given a detected candidate (i.e. a cluster of
suspicious voxels), we compute a set of contextual features, including its distance to the airway based on local
distance transform and its relative position to the artery based on fast tensor voting and Hessian "vesselness"
scores. Our tests on unseen cases show that these features can reduce the lymph FPs by 59%, while improving
the overall sensitivity by 3.4%.
The purpose of this study was to investigate feasibility of computer-aided detection of masses and calcification clusters in breast tomosynthesis images and obtain reliable estimates of sensitivity and false positive rate on an independent test set. Automatic mass and calcification detection algorithms developed for film and digital mammography images were applied without any adaptation or retraining to tomosynthesis projection images. Test set contained 36 patients including 16 patients with 20 known malignant lesions, 4 of which were missed by the radiologists in conventional mammography images and found only in retrospect in tomosynthesis. Median filter was applied to tomosynthesis projection images. Detection algorithm yielded 80% sensitivity and 5.3 false positives per breast for calcification and mass detection algorithms combined. Out of 4 masses missed by radiologists in conventional mammography images, 2 were found by the mass detection algorithm in tomosynthesis images.
The purpose of this study is to develop a method for segmenting lesions on Dynamic Contrast-Enhanced (DCE) breast MRI. DCE breast MRI, in which the breast is imaged before, during, and after the administration of a contrast agent, enables a truly 3D examination of breast tissues. This functional angiogenic imaging technique provides noninvasive assessment of microcirculatory characteristics of tissues in addition to traditional anatomical structure information. Since morphological features and kinetic curves from segmented lesions are to be used for diagnosis and treatment decisions, lesion segmentation is a key pre-processing step for classification. In our study, the ROI is defined by a bounding box containing the enhancement region in the subtraction image, which is generated by subtracting the pre-contrast image from 1st post-contrast image. A maximum a posteriori (MAP) estimate of the class membership (lesion vs. non-lesion) for each voxel is obtained using the Iterative Conditional Mode (ICM) method. The prior distribution of the class membership is modeled as a multi-level logistic model, a Markov Random Field model in which the class membership of each voxel is assumed to depend upon its nearest neighbors only. The likelihood distribution is assumed to be Gaussian. The parameters of each Gaussian distribution are estimated from a dozen voxels manually selected as representative of the class. The experimental segmentation results demonstrate anatomically plausible breast tissue segmentation and the predicted class membership of voxels from the interactive segmentation algorithm agrees with the manual classifications made by inspection of the kinetic enhancement curves. The proposed method is advantageous in that it is efficient, flexible, and robust.
The Computed Tomography (CT) modality shows not only the body of the patient in the volumes it generates, but also the clothing, the cushion and the table. This might be a problem especially for two applications. The first is 3D visualization, where the table has high density parts that might hide regions of interest. The second is registration of acquisitions obtained at different time points; indeed, the table and cushions might be visible in one data set only, and their positions and shapes may vary, making the registration less accurate. An automatic approach for extracting the body would solve those problems. It should be robust, reliable, and fast. We therefore propose a multi-scale method based on deformable models. The idea is to move a surface across the image that attaches to the boundaries of the body. We iteratively compute forces which take into account local information around the surface. Those make it move through the table but ensure that it stops when coming close to the body. Our model has elastic properties; moreover, we take into account the fact that some regions in the volume convey more information than others by giving them more weight. This is done by using normalized convolution when regularizing the surface. The algorithm*, tested on a database of over a hundred volumes of
whole body, chest or lower abdomen, has proven to be very efficient, even for volumes with up to 900 slices, providing accurate results in an average time of 6 seconds. It is also robust against noise and variations of scale and table's shape.
X-ray digital subtraction angiography (DSA) images frequently suffer from misregistration artifacts. Commercial systems use whole-image manual and semi-automated registration techniques that can be tedious to use. Frequently, patient motion leads to complex artifacts that whole-image registration cannot remove. Available computer technology makes warping registration feasible and timely. We evaluated 6 different warping registration algorithms using 10 subjects. Image quality of subtracted images was evaluated using numerical scoring to specific image quality questions. To aid image quality comparison, images were displayed side-by-side on a single 21-inch monitor. The case mix consisted of 15 DSA images, with significant substraction artifacts, taken from the feet, legs, abdomen, chest and head. In 92% of cases, warping registration dramatically improved subtraction image quality while whole-image translation methods showed little or no improvement. It was also found that the most successful warping method varied from case to case. Based on this study, we propose a combination of warping registration techniques.
Spiral CT-angiography (CTA) is a new, minimally invasive technique for vascular imaging. A key task in CTA is to suppress bone and other dense tissues, such as calcification, in order to visualize the vessels of interest. Common practice in CTA employs automatic or semiautomatic bone editing techniques. However, editing is not possible where the bone structures are complex, and particularly when they are close to vessels. In addition, it is quite unlikely that any automatic editing technique would succeed in removing calcification. An alternative approach is to extend conventional digital subtraction techniques (DSA) from two dimensions to three dimensions. The potential advantages of digital subtraction CTA are; (1) fully automatic bone removal, (2) elimination of calcification, and (3) reducing the concentration of contrast medium. This paper presents a 3D flexible registration technique that brings a pre-contrast (native) volume to register with a post-contrast (contrast) volume prior to subtraction. Two volumes are treated to be composed of blocks; each block in one data set is translated, rotated, and scaled in three dimensions to match the corresponding block of the other volume. In essence, flexible matching is realized by rigid body motion and scaling of a large number of blocks. The proposed algorithm has been successfully applied on a number of clinical studies.
This paper describes a method for obtaining a composite focused image from a monocular image sequence. The image sequence is obtained using a novel non-frontal camera that has sensor elements at different distances from the lens. This paper first describes the motivation behind the non-frontal camera, followed by the description of an algorithm to obtain a focused image of a large scene. Large scenes are scenes that are deep and wide (panoramic). Consequently, the camera has to be panned in order to image all objects/surfaces of interest. The described algorithm integrates panning and generation of focused images. Results of experiments to generate extended depth of field images of wide scenes are also shown.
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