Computed tomographic colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. In current practice, a patient will be scanned twice during the CTC examination - once supine and once prone. In order to assist the radiologists in evaluating colon polyp candidates in both scans, we expect the computer aided detection (CAD) system can provide not only the locations of suspicious polyps, but also the possible matched pairs of polyps in two scans. In this paper, we propose a new automated matching method based on the extracted features of polyps by using principal component analysis (PCA) and Support Vector Machines (SVMs). Our dataset comes from the 104 CT scans of 52 patients with supine and prone positions collected from three medical centers. From it we constructed two groups of matched polyp candidates according to the size of true polyps: group A contains 12 true polyp pairs (> 9 mm) and 454 false pairs; group B contains 24 true polyp pairs (6-9 mm) and 514 false pairs. By using PCA, we reduced the dimensions of original data (with 157 attributes) to 30 dimensions. We did leave-one-patient-out test on the two groups of data. ROC analysis shows that it is easier to match bigger polyps than that of smaller polyps. On group A data, when false alarm probability is 0.18, the sensitivity of SVM achieves 0.83 which shows that automated matching of polyp candidates is practicable for clinical applications.
Pulmonary arterial hypertension is a known complication associated with sickle-cell disease; roughly 75% of sickle cell
disease-afflicted patients have pulmonary arterial hypertension at the time of death. This prospective study investigates
the potential of image analysis to act as a surrogate for presence and extent of disease, and whether the size change of
the pulmonary arteries of sickle cell patients could be linked to sickle-cell associated pulmonary hypertension.
Pulmonary CT-Angiography scans from sickle-cell patients were obtained and retrospectively analyzed. Randomly
selected pulmonary CT-Angiography studies from patients without sickle-cell anemia were used as negative controls.
First, images were smoothed using anisotropic diffusion. Then, a combination of fast marching and geodesic active
contours level sets were employed to segment the pulmonary artery. An algorithm based on fast marching methods was
used to compute the centerline of the segmented arteries. From the centerline, the diameters at the pulmonary trunk and
first branch of the pulmonary arteries were measured automatically. Arterial diameters were normalized to the width of
the thoracic cavity, patient weight and body surface. Results show that the pulmonary trunk and first right and left
pulmonary arterial branches at the pulmonary trunk junction are significantly larger in diameter with increased blood
flow in sickle-cell anemia patients as compared to controls (p values of 0.0278 for trunk and 0.0007 for branches). CT
with image processing shows great potential as a surrogate indicator of pulmonary hemodynamics or response to
therapy, which could be an important tool for drug discovery and noninvasive clinical surveillance.
The detection of polyps in virtual colonoscopy is an active area of research. One of the critical elements in detecting
cancerous polyps using virtual colonoscopy, especially in conjunction with computer-aided detection, is the accurate
segmentation of the colon wall. The large CT attenuation difference between the lumen and inner, mucosal layer of the
colon wall makes the segmentation of the lumen easily performed by traditional threshold segmentation techniques.
However, determining the location of the colon outer wall is often difficult due to the low contrast difference between
the colon wall's outer serosal layer and the fat surrounding the colon. We have developed an automatic, level set based
method to determine from a CT colonography scan the location of the colon inner boundary and the colon outer wall
boundary. From the location of the inner and outer colon wall boundaries, the wall thickness throughout the colon can
be computed. Color mapping of the wall thickness on the colon surface allows for easy visual determination of
potential regions of interest. Since the colon wall tends to be thicker at polyp locations, potential polyps also can be
detected automatically at sites of increased colon wall thickness. This method was validated on several CT
colonography scans containing optical colonoscopy-proven polyps. The method accurately determined thicker colonic
wall regions in areas where polyps are present in the ground truth datasets and detected the polyps at a false positive rate
between 44.4% and 82.8% lower than a state-of-the-art curvature-based method for initial polyp detection.
Delineation of objects within medical images is often difficult to perform reproducibly when one relies upon hand-segmentation. To avoid inter- and intra-user variability, a semi-automatic segmentation method can more accurately and consistently determine the object boundaries. This paper presents a semi-automatic process for determining the length and volume of the spinal cord between adjacent pairs of intervertebral discs and the total length and volume of the spinal cord. A level set segmentation was performed on MRI data with user selected landmarks in order to obtain a segmentation of the spinal cord. The length and volume measurements were performed on 20 segments from C1 to L1 with five sets of user selected landmarks. Our results show that the average spinal cord segment length was 21.55 mm with a standard deviation of 25.11% and the average spinal cord segment volume was 2,217.16 mm<sup>3</sup> with a standard deviation of 80.51%. The measurement variability of a single anatomical length across multiple trials of different sets of seed points was three orders of magnitude lower (0.06%) than the variability across different anatomical lengths (25.23%), while the measurement variability of a single anatomical volume across multiple trials of different sets of seed points was two orders of magnitude lower (0.37%) than the variability across different anatomical volumes (79.24%). Our method has been demonstrated to be potentially insensitive to intra- and inter-user variability.
Virtual colonoscopy is becoming a more prevalent way to diagnose colon cancer. One of the critical elements in detecting cancerous polyps using virtual colonoscopy, especially in conjunction with computer-aided detection of polyps, is that the colon be sufficiently distended. We have developed an automatic method to determine from a CT scan what percentage of the colon is distended by 1cm or larger and compared our method with a radiologist's assessment of quality of the scan with respect to successful colon polyp detection. A radiologist grouped 41 CT virtual colonoscopy scans into three groups according to the degree of colonic distention, "well", "medium", and "poor". We also employed a subvoxel accurate centerline algorithm and a subvoxel accurate distance transform to each dataset to measure the colon distention along the centerline. To summarize the colonic distention with a single value relevant for polyp detection, the distention score, we recorded the percentage of centerline positions in which the colon distention was 1cm or larger. We then compared the radiologist's assessment and the computed results. The sorting of all datasets according to the distention score agreed with the radiologist's assessment. The "poor" cases had a mean and standard deviation score of 78.4% ± 5.2%, the "medium" cases measured 88.7% ± 1.9%, and the "well" cases 98.8% ± 1.5%. All categories were shown to be significantly different from each other using unpaired two sample t-tests. The presented colonic distention score is an accurate method for assessing the quality of colonic distention for CT colonography.