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<sup>∗</sup> 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.
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<sup>∗</sup> 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.
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 prototype<sup>1</sup>.
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
prototype<sup>1</sup> results showing significant improvement of CAD sensitivity at much better false positive rate on thick-slice
CT images are presented.
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.
Advances in multi-detector technology have made CT pulmonary angiography (CTPA) a popular radiological tool for
pulmonary emboli (PE) detection. CTPA provide rich detail of lung anatomy and is a useful diagnostic aid in highlighting
even very small PE. However analyzing hundreds of slices is laborious and time-consuming for the practicing radiologist
which may also cause misdiagnosis due to the presence of various PE look-alike.
Computer-aided diagnosis (CAD) can be a potential second reader in providing key diagnostic information. Since
PE occurs only in vessel arteries, it is important to mark this region of interest (ROI) during CAD preprocessing. In
this paper, we present a new lung and vessel segmentation algorithm for extracting contrast-enhanced vessel ROI in
CTPA. Existing approaches to segmentation either provide only the larger lung area without highlighting the vessels or
is computationally prohibitive.
In this paper, we propose a hybrid lung and vessel segmentation which uses an initial lung ROI and determines the
vessels through a series of refinement steps. We first identify a coarse vessel ROI by finding the "holes" from the lung
ROI. We then use the initial ROI as seed-points for a region-growing process while carefully excluding regions which are
not relevant. The vessel segmentation mask covers 99% of the 259 PE from a real-world set of 107 CTPA. Further, our
algorithm increases the net sensitivity of a prototype CAD system by 5-9% across all PE categories in the training and
validation data sets. The average run-time of algorithm was only 100 seconds on a standard workstation.