A major challenge in the current computer-aided detection (CADe) of hepatocellular carcinomas (HCCs) in contrastenhanced
hepatic CT is to reduce the number of false-positive (FP) detections while maintaining a high sensitivity level.
In this paper, we propose a feature selection method based on a sequential forward floating selection procedure coupled
with a linear discriminant analysis classifier to improve the classification performance in computerized detection of
HCCs in contrast-enhanced hepatic CT. The proposed method selected the most relevant features that would maximize
the partial area under the receiver-operating-characteristic (ROC) curve (partial AUC) value, which would essentially
lead to the maximum classification performance in the computer-aided detection scheme in a clinical setting. The partial
AUC value is defined as the normalized AUC value in the high sensitivity region of the ROC curve, which is of clinical
importance. In order to test the performance of the proposed method, we compared it against the popular stepwise
feature selection method based on Wilks' lambda and a recently developed maximal AUC feature selection for an HCC
database (23 HCCs and 1279 non-HCCs). We extracted 88 morphologic, gray-level-based, and texture features from the
segmented lesion candidate regions in the hepatic CT images. The proposed method selected 9 features and achieved
100% sensitivity at 5.5 FPs per patient. Experiments showed a significant improvement in the performance of the
classifier with the proposed feature selection method over that with the popular stepwise feature selection based on
Wilks' lambda (17.3 FPs per patient) and the maximal AUC feature selection (10.0 FPs per patient) in terms of AUC
values and FP rates.
Computer-aided detection (CADe) has been investigated for assisting radiologists in detecting polyps in CT
colonography (CTC). One of the major challenges in current CADe of polyps in CTC is to improve the specificity
without sacrificing the sensitivity. We have developed several CADe schemes based on a massive-training framework
with different nonlinear regression models such as neural network regression, support vector regression, and Gaussian
process regression. Individual CADe schemes based on different nonlinear regression models, however, achieved
comparable results. In this paper, we propose to use the AdaBoost algorithm to combine different regression models in
CADe schemes for improving the specificity without sacrificing the sensitivity. To test the performance of the proposed
approach, we compared it with individual regression models in the distinction between polyps and various types of false
positives (FPs). Our CTC database consisted of 246 CTC datasets obtained from 123 patients in the supine and prone
positions. The testing set contained 93 patients including 19 polyps in seven patients and 86 negative patients with 474
FPs produced by an original CADe scheme. The AdaBoost algorithm combining multiple massive-training regression
models achieved a performance that was higher than each individual regression model, yielding a 94.7% (18/19) bypolyp
sensitivity at an FP rate of 2.0 (188/93) per patient in a leave-one-lesion-out cross validation test.
Malignant liver tumors such as hepatocellular carcinoma (HCC) account for 1.25 million deaths each year worldwide.
Early detection of HCC is sometimes difficult on CT images because the attenuation of HCC is often similar to that of
normal liver parenchyma. Our purpose was to develop computer-aided detection (CADe) of HCC using both arterial
phase (AP) and portal-venous phase (PVP) of contrast-enhanced CT images. Our scheme consisted of liver
segmentation, tumor candidate detection, feature extraction and selection, and classification of the candidates as HCC or
non-lesions. We used a 3D geodesic-active-contour model coupled with a level-set algorithm to segment the liver. Both
hyper- and hypo-dense tumors were enhanced by a sigmoid filter. A gradient-magnitude filter followed by a watershed
algorithm was applied to the tumor-enhanced images for segmenting closed-contour regions as HCC candidates.
Seventy-five morphologic and texture features were extracted from the segmented candidate regions in both AP and
PVP images. To select most discriminant features for classification, we developed a sequential forward floating feature
selection method directly coupled with a support vector machine (SVM) classifier. The initial CADe before the
classification achieved a 100% (23/23) sensitivity with 33.7 (775/23) false positives (FPs) per patient. The SVM with
four selected features removed 96.5% (748/775) of the FPs without any removal of the HCCs in a leave-one-lesion-out
cross-validation test; thus, a 100% sensitivity with 1.2 FPs per patient was achieved, whereas CADe using AP alone
produced 6.4 (147/23) FPs per patient at the same sensitivity level.
Current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. Our
purpose was to develop an automated measurement of polyp volume in CT colonography (CTC). We developed a
computerized segmentation scheme for measuring polyp volume in CTC, which consisted of extraction of a highly
polyp-like seed region based on the Hessian matrix, segmentation of polyps by use of a 3D volume-growing technique,
and sub-voxel refinement to reduce a bias of segmentation. Our database consisted of 30 polyp views (15 polyps) in
CTC scans from 13 patients. To obtain "gold standard," a radiologist outlined polyps in each slice and calculated
volumes by summation of areas. The measurement study was repeated three times at least one week apart for minimizing
a memory effect bias. We used the mean volume of the three studies as "gold standard." Our measurement scheme
yielded a mean polyp volume of 0.38 cc (range: 0.15-1.24 cc), whereas a mean "gold standard" manual volume was 0.40
cc (range: 0.15-1.08 cc). The mean absolute difference between automated and manual volumes was 0.11 cc with
standard deviation of 0.14 cc. The two volumetrics reached excellent agreement (intra-class correlation coefficient was
0.80) with no statistically significant difference (p(F≤f) = 0.42). Thus, our automated scheme efficiently provides
accurate polyp volumes for radiologists.
Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar
density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We
developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing
filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an
edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching
algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour
segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the
liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen
prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated
liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean
liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean
absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard"
manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference
(p(F≤f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate
way of measuring liver volumes.
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