Non-interventional diagnostics (CT or MR) enables early identification of diseases like cancer. Often, lesion growth assessment done during follow-up is used to distinguish between benign and malignant ones. Thus correspondences need to be found for lesions localized at each time point. Manually matching the radiological
findings can be time consuming as well as tedious due to possible differences in orientation and position between
scans. Also, the complicated nature of the disease makes the physicians to rely on multiple modalities (PETCT, PET-MR) where it is even more challenging. Here, we propose an automatic feature-based matching that is robust to change in organ volume, subpar or no registration that can be done with very less computations. Traditional matching methods rely mostly on accurate image registration and applying the resulting deformation map on the findings coordinates. This has disadvantages when accurate registration is time-consuming or may not be possible due to vast organ volume differences between scans. Our novel matching proposes supervised learning by taking advantage of the underlying CAD features that are already present and considering the matching as a classification problem. In addition, the matching can be done extremely fast and at reasonable accuracy even when the image registration fails for some reason. Experimental results∗ on real-world multi-time point thoracic CT data showed an accuracy of above 90% with negligible false positives on a variety of registration scenarios.
Recent studies have shown that low dose computed tomography (LDCT) can be an effective screening tool to
reduce lung cancer mortality. Computer-aided detection (CAD) would be a beneficial second reader for radiologists
in such cases. Studies demonstrate that while iterative reconstructions (IR) improve LDCT diagnostic quality, it however degrades CAD performance significantly (increased false positives) when applied directly. For improving CAD performance, solutions such as retraining with newer data or applying a standard preprocessing technique may not be suffice due to high prevalence of CT scanners and non-uniform acquisition protocols. Here, we present a learning-based framework that can adaptively transform a wide variety of input data to boost an existing CAD performance. This not only enhances their robustness but also their applicability in clinical workflows. Our solution consists of applying a suitable pre-processing filter automatically on the given image based on its characteristics. This requires the preparation of ground truth (GT) of choosing an appropriate filter resulting in improved CAD performance. Accordingly, we propose an efficient consolidation process with a novel metric. Using key anatomical landmarks, we then derive consistent feature descriptors for the classification scheme that then uses a priority mechanism to automatically choose an optimal preprocessing filter. We demonstrate CAD prototype∗ performance improvement using hospital-scale datasets acquired from North America, Europe and Asia. Though we demonstrated our results for a lung nodule CAD, this scheme is straightforward to extend to other post-processing tools dedicated to other organs and modalities.
There is an increasing need to provide end-users with seamless and secure access to healthcare information acquired
from a diverse range of sources. This might include local and remote hospital sites equipped with different vendors and
practicing varied acquisition protocols and also heterogeneous external sources such as the Internet cloud. In such
scenarios, image post-processing tools such as CAD (computer-aided diagnosis) which were hitherto developed using a
smaller set of images may not always work optimally on newer set of images having entirely different characteristics.
In this paper, we propose a framework that assesses the quality of a given input image and automatically applies an
appropriate pre-processing method in such a manner that the image characteristics are normalized regardless of its
source. We focus mainly on medical images, and the objective of the said preprocessing method is to standardize the
performance of various image processing and workflow applications like CAD to perform in a consistent manner. First,
our system consists of an assessment step wherein an image is evaluated based on criteria such as noise, image
sharpness, etc. Depending on the measured characteristic, we then apply an appropriate normalization technique thus
giving way to our overall pre-processing framework. A systematic evaluation of the proposed scheme is carried out on
large set of CT images acquired from various vendors including images reconstructed with next generation iterative
methods. Results demonstrate that the images are normalized and thus suitable for an existing LungCAD prototype1.
Computer-aided diagnosis (CAD) systems for detection of lung nodules have been an active topic of research for last few
years. It is desirable that a CAD system should generate very low false positives (FPs) while maintaining high
sensitivity. This work aims to reduce the number of false positives occurring at vessel bifurcation point. FPs occur quite
frequently on vessel branching point due to its shape which can appear locally spherical due to the intrinsic geometry of
intersecting tubular vessel structures combined with partial volume effects and soft tissue attenuation appearance
surrounded by parenchyma.
We propose a model-based technique for detection of vessel branching points using skeletonization, followed by branch-point
analysis. First we perform vessel structure enhancement using a multi-scale Hessian filter to accurately segment
tubular structures of various sizes followed by thresholding to get binary vessel structure segmentation [6]. A modified
Reebgraph [7] is applied next to extract the critical points of structure and these are joined by a nearest neighbor criterion
to obtain complete skeletal model of vessel structure. Finally, the skeletal model is traversed to identify branch points,
and extract metrics including individual branch length, number of branches and angle between various branches. Results
on 80 sub-volumes consisting of 60 actual vessel-branching and 20 solitary solid nodules show that the algorithm
identified correctly vessel branching points for 57 sub-volumes (95% sensitivity) and misclassified 2 nodules as vessel
branch. Thus, this technique has potential in explicit identification of vessel branching points for general vessel analysis, and could be useful in false positive reduction in a lung CAD system.
Common chest CT clinical workflows for detecting lung nodules use a large slice thickness protocol (typically 5 mm).
However, most existing CAD studies are performed on a thin slice data (0.3-2 mm) available on state-of-the art scanners.
A major challenge for the widespread clinical use of Lung CAD is the concurrent availability of both thick and thin
resolutions for use by radiologist and CAD respectively. Having both slice thickness reconstructions is not always
possible based on the availability of scanner technologies, acquisition parameters chosen at remote site, and transmission
and archiving constraints that may make transmission and storage of large data impracticable. However, applying current
thin-slice CAD algorithms on thick slice cases outside their designed acquisition parameters may result in degradation of
sensitivity and high false-positive rate making them clinically unacceptable. Therefore a CAD system that can handle
thicker slice acquisitions is desirable to address those situations.
In this paper, we propose a CAD system which works directly on thick slice scans. We first propose a multi-stage
classifier based CAD system for detecting lung nodules in such data. Furthermore, we propose different gating systems
adapted for thick slice scans. The proposed gating schemes are based on: 1. wall-attached and non wall-attached. 2.
central and non-central region. These gating schemes can be used independently or combined as well. Finally, we present
prototype1 results showing significant improvement of CAD sensitivity at much better false positive rate on thick-slice
CT images are presented.
Coronary artery disease is the end result of the accumulation of atheromatous plaques within the walls of coronary
arteries and is the leading cause of death worldwide. Computed tomography angiography (CTA) has been proved to be
very useful for accurate noninvasive diagnosis and quantification of plaques. However, the existing methods to measure
the stenosis in the plaques are not accurate enough in mid and distal segments where the vessels become narrower. To
alleviate this, we propose a method that consists of three stages namely, automatic extraction of coronary vessels; vessels
straightening; lumen extraction and stenosis evaluation.
In the first stage, the coronary vessels are segmented using a parametric approach based on circular vessel model at each
point on the centerline. It is assumed that centerline information is available in advance. Vessel straightening in the
second stage performs multi-planar reformat (MPR) to straighten the curved vessels. MPR view of a vessel helps to
visualize and measure the plaques better. On the straightened vessel, lumen and vessel wall are segregated using a
nearest neighbor classification. To detect the plaques with severe stenosis in the vessel lumen, we propose a "Diameter
Luminal Stenosis" method for analyzing the smaller segments of the vessel. Proposed measurement technique identifies
the segments that have plaques and reports the top three severely stenosed segments. Proposed algorithm is applied on 24
coronary vessels belonging to multiple cases acquired from Sensation 64 - slice CT and initial results are promising.
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.
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