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.
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.
Computer-aided diagnosis (CAD) systems usually require information about regions of interest in images, like:
lungs (for nodule detection), colon (for identifying polyps), etc. Many times, it is computationally intensive to
process large data sets as in CT to find these areas of interest. A fast and accurate recognition of the different
regions of interest in the human body from images is therefore necessary. In this paper we propose a fast and
efficient algorithm that can detect the organs of interest in a CT volume and estimate their sizes. Instead of
analyzing the whole 3D volume; which is computationally expensive, a binary search technique is adapted to
search in a few slices. The slices selected in the search process is segmented and different regions are labeled.
Decision over whether the image belongs to a lung or colon or both is based on the count of lung/colon pixels
in the slice. Once the detection is done we look for the start and end slice of the body part to have an estimate
of their sizes. The algorithm involves certain search decisions based on some predefined threshold values which
are empirically chosen from a training data set. The effectiveness of our technique is confirmed by applying
it on an independent test data set. Detection accuracy of 100% is obtained on a test set. This algorithm can
be integrated in a CAD system for running the right application, or can be used in pre-sets for visualization
purposes and other post-processing like image registration etc.
Chest X-ray (CXR) data is a 2D projection image. The main drawback of such an image is that each pixel
of it represents a volumetric integration. This poses a challenge in detection and estimation of nodules and
their characteristics. Due to human anatomy there are a lot of lung structures which can be falsely identified as
nodules in a projection data. Detection of nodules with a large number of false positives (FP) adds more work
for the radiologists.
With the help of CAD algorithms we aim to identify regions which cause higher FP readings or provide
additional information for nodule detection based on the human anatomy.
Different lung regions have different image characteristics we take advantage of this and propose an automatic
lung partitioning into vessel, apical, basal and exterior pulmonary regions. Anatomical landmarks like aortic
arch and end of cardiac-notch along-with inter intra-rib width and their shape characteristics were used for this
partitioning. Likelihood of FPs is more in vessel, apical and exterior pulmonary regions due to rib-crossing,
overlap of vessel with rib and vessel branching. For each of these three cases, special features were designed
based on histogram of rib slope and the structural properties of rib segments information. These features were
assigned different weights based on the partitioning.
An experiment was carried out using a prototype CAD system 150 routine CXR studies were acquired from
three institutions (24 negatives, rest with one or more nodules). Our algorithm provided a sensitivity of 70.4%
with 5 FP/image for cross-validation without partition. Inclusion of the proposed techniques increases the
sensitivity to 78.1% with 4.1 FP/image.
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