A straightforward approach to computer-aided polyp detection using a polyp-specific volumetric feature in CT colonography

This study presents a straightforward approach to computer-aided polyp detection and explores its advantages and future potential. A straightforward computer-aided polyp detection (CAD) scheme was developed that consisted of colon wall segmentation, a polyp-specific volumetric filter, and the counting and thresholding of cluster volume sizes. 65 patients had undergone the bowel cleaning scheme without fecal tagging and the optical colonoscopy (OC) and CT colonography (CTC) were performed. The polyp sizes determined by OC were used as reference measurements. The CTC dataset with 103 polyps were divided into training and test datasets. After tuning for the optimal parameter settings, the per-polyp sensitivities of the developed CAD scheme for clinically relevant polyps (≥6 mm) were 100% at 8.5 false positives (FPs)/patient using the training dataset, and 93.3% at 7.7 FPs/patient using the test dataset. The developed CAD scheme was found to have a relatively high detection performance, easily optimized parameter settings, and an easily understood internal operation.     

Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes

One of the limitations of the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is a relatively large number of false-positive (FP) detections. Rectal tubes (RTs) are one of the typical sources of FPs because a portion of a RT, especially a portion of a bulbous tip, often exhibits a cap-like shape that closely mimics the appearance of a small polyp. Radiologists can easily recognize and dismiss RT-induced FPs; thus, they may lose their confidence in CAD as an effective tool if the CAD scheme generates such "obvious" FPs due to RTs consistently. In addition, RT-induced FPs may distract radiologists from less common true positives in the rectum. Therefore, removal RT-induced FPs as well as other types of FPs is desirable while maintaining a high sensitivity in the detection of polyps. We developed a three-dimensional (3D) massive-training artificial neural network (MTANN) for distinction between polyps and RTs in 3D CTC volumetric data. The 3D MTANN is a supervised volume-processing technique which is trained with input CTC volumes and the corresponding "teaching" volumes. The teaching volume for a polyp contains a 3D Gaussian distribution, and that for a RT contains zeros for enhancement of polyps and suppression of RTs, respectively. For distinction between polyps and nonpolyps including RTs, a 3D scoring method based on a 3D Gaussian weighting function is applied to the output of the trained 3D MTANN. Our database consisted of CTC examinations of 73 patients, scanned in both supine and prone positions (146 CTC data sets in total), with optical colonoscopy as a reference standard for the presence of polyps. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. These CTC cases were subjected to our previously reported CAD scheme that included centerline-based segmentation of the colon, shape-based detection of polyps, and reduction of FPs by use of a Bayesian neural network based on geometric and texture features. Application of this CAD scheme yielded 96.4% (27/28) by-polyp sensitivity with 3.1 (224/73) FPs per patient, among which 20 FPs were caused by RTs. To eliminate the FPs due to RTs and possibly other normal structures, we trained a 3D MTANN with ten representative polyps and ten RTs, and applied the trained 3D MTANN to the above CAD true- and false-positive detections. In the output volumes of the 3D MTANN, polyps were represented by distributions of bright voxels, whereas RTs and other normal structures partly similar to RTs appeared as darker voxels, indicating the ability of the 3D MTANN to suppress RTs as well as other normal structures effectively. Application of the 3D MTANN to the CAD detections showed that the 3D MTANN eliminated all RT-induced 20 FPs, as well as 53 FPs due to other causes, without removal of any true positives. Overall, the 3D MTANN was able to reduce the FP rate of the CAD scheme from 3.1 to 2.1 FPs per patient (33% reduction), while the original by-polyp sensitivity of 96.4% was maintained.     

Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography

One of the major challenges in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the reduction of false-positive detections (FPs) without a concomitant reduction in sensitivity. A large number of FPs is likely to confound the radiologist's task of image interpretation, lower the radiologist's efficiency, and cause radiologists to lose their confidence in CAD as a useful tool. Major sources of FPs generated by CAD schemes include haustral folds, residual stool, rectal tubes, the ileocecal valve, and extra-colonic structures such as the small bowel and stomach. Our purpose in this study was to develop a method for the removal of various types of FPs in CAD of polyps while maintaining a high sensitivity. To achieve this, we developed a "mixture of expert" three-dimensional (3D) massive-training artificial neural networks (MTANNs) consisting of four 3D MTANNs that were designed to differentiate between polyps and four categories of FPs: (1) rectal tubes, (2) stool with bubbles, (3) colonic walls with haustral folds, and (4) solid stool. Each expert 3D MTANN was trained with examples from a specific non-polyp category along with typical polyps. The four expert 3D MTANNs were combined with a mixing artificial neural network (ANN) such that different types of FPs could be removed. Our database consisted of 146 CTC datasets obtained from 73 patients whose colons were prepared by standard pre-colonoscopy cleansing. Each patient was scanned in both supine and prone positions. Radiologists established the locations of polyps through the use of optical-colonoscopy reports. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. The CTC cases were subjected to our previously reported CAD method consisting of centerline-based extraction of the colon, shape-based detection of polyp candidates, and a Bayesian-ANN-based classification of polyps. The original CAD method yielded 96.4% (27/28) by-polyp sensitivity with an average of 3.1 (224/73) FPs per patient. The mixture of expert 3D MTANNs removed 63% (142/224) of the FPs without the loss of any true positive; thus, the FP rate of our CAD scheme was improved to 1.1 (82/73) FPs per patient while the original sensitivity was maintained. By use of the mixture of expert 3D MTANNs, the specificity of a CAD scheme for detection of polyps in CTC was substantially improved while a high sensitivity was maintained.     

Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: pilot study

Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed a two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.     

Computerized detection of colorectal masses in CT colonography based on fuzzy merging and wall-thickening analysis

In recent years, several computer-aided detection (CAD) schemes have been developed for the detection of polyps in CT colonography (CTC). However, few studies have addressed the problem of computerized detection of colorectal masses in CTC. This is mostly because masses are considered to be well visualized by a radiologist because of their size and invasiveness. Nevertheless, the automated detection of masses would naturally complement the automated detection of polyps in CTC and would produce a more comprehensive computer aid to radiologists. Therefore, in this study, we identified some of the problems involved with the computerized detection of masses, and we developed a scheme for the computerized detection of masses that can be integrated into a CAD scheme for the detection of polyps. The performance of the mass detection scheme was evaluated by application to clinical CTC data sets. CTC was performed on 82 patients with helical CT scanners and reconstruction intervals of 1.0-5.0 mm in the supine and prone positions. Fourteen patients (17%) had a total of 14 masses of 30-50 mm, and sixteen patients (20%) had a total of 30 polyps 5-25 mm in diameter. Four patients had both polyps and masses. Fifty-six of the patients (68%) were normal. The CTC data were interpolated linearly to yield isotropic data sets, and the colon was extracted by use of a knowledge-guided segmentation technique. Two methods, fuzzy merging and wall-thickening analysis, were developed for the detection of masses. The fuzzy merging method detected masses with a significant intraluminal component by separating the initial CAD detections of locally cap-like shapes within the colonic wall into mass candidates and polyp candidates. The wall-thickening analysis detected nonintraluminal masses by searching the colonic wall for abnormal thickening. The final regions of the mass candidates were extracted by use of a level set method based on a fast marching algorithm. False-positive (FP) detections were reduced by a quadratic discriminant classifier. The performance of the scheme was evaluated by use of a leave-one-out (round-robin) method with by-patient elimination. All but one of the 14 masses, which was partially cut off from the CTC data set in both supine and prone positions, were detected. The fuzzy merging method detected 11 of the masses, and the wall-thickening analysis detected 3 of the masses including all nonintraluminal masses. In combination, the two methods detected 13 of the 14 masses with 0.21 FPs per patient on average based on the leave-one-out evaluation. Most FPs were generated by extrinsic compression of the colonic wall that would be recognized easily and quickly by a radiologist. The mass detection methods did not affect the result of the polyp detection. The results indicate that the scheme is potentially useful in providing a high-performance CAD scheme for the detection of colorectal neoplasms in CTC.     

Improved classifier for computer-aided polyp detection in CT colonography by nonlinear dimensionality reduction

Computer-aided detection (CAD) has been shown to be feasible for polyp detection on computed tomography (CT) scans. After initial detection, the dataset of colonic polyp candidates has large-scale and high dimensional characteristics. In this article, we propose a nonlinear dimensionality reduction method based on diffusion map and locally linear embedding (DMLLE) for large-scale datasets. By selecting partial data as landmarks, we first map these points into a low dimensional embedding space using the diffusion map. The embedded landmarks can be viewed as a skeleton of whole data in the low dimensional space. Then by using the locally linear embedding algorithm, nonlandmark samples are mapped into the same low dimensional space according to their nearest landmark samples. The local geometry is preserved in both the original high dimensional space and the embedding space. In addition, DMLLE provides a faithful representation of the original high dimensional data at coarse and fine scales. Thus, it can capture the intrinsic distance relationship between samples and reduce the influence of noisy features, two aspects that are crucial to achieving high classifier performance. We applied the proposed DMLLE method to a colonic polyp dataset of 175 269 polyp candidates with 155 features. Visual inspection shows that true polyps with similar shapes are mapped to close vicinity in the low dimensional space. We compared the performance of a support vector machine (SVM) classifier in the low dimensional embedding space with that in the original high dimensional space, SVM with principal component analysis dimensionality reduction and SVM committee using feature selection technology. Free-response receiver operating characteristic analysis shows that by using our DMLLE dimensionality reduction method, SVM achieves higher sensitivity with a lower false positive rate compared with other methods. For 6-9 mm polyps (193 true polyps contained in test set), when the number of false positives per patient is 9, SVM with DMLLE improves the average sensitivity from 70% to 83% compared with that of an SVM committee classifier which is a state-of-the-art method for colonic polyp detection (p<0.001).     

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study.     

Development of a synthetic phantom for the selection of optimal scanning parameters in CAD-CT colonography

The aim of this paper is to present the development of a synthetic phantom that can be used for the selection of optimal scanning parameters in computed tomography (CT) colonography. In this paper we attempt to evaluate the influence of the main scanning parameters including slice thickness, reconstruction interval, field of view, table speed and radiation dose on the overall performance of a computer aided detection (CAD)-CTC system. From these parameters the radiation dose received a special attention, as the major problem associated with CTC is the patient exposure to significant levels of ionising radiation. To examine the influence of the scanning parameters we performed 51 CT scans where the spread of scanning parameters was divided into seven different protocols. A large number of experimental tests were performed and the results analysed. The results show that automatic polyp detection is feasible even in cases when the CAD-CTC system was applied to low dose CT data acquired with the following protocol: 13 mAs/rotation with collimation of 1.5 mm x 16 mm, slice thickness of 3.0mm, reconstruction interval of 1.5 mm, table speed of 30 mm per rotation. The CT phantom data acquired using this protocol was analysed by an automated CAD-CTC system and the experimental results indicate that our system identified all clinically significant polyps (i.e. larger than 5 mm).     

CT colonography: does improved z resolution help computer-aided polyp detection?

Multislice helical CT offers several retrospective choices of longitudinal (z) resolution at a given detector collimation setting. We sought to determine the effect of z resolution on the performance of a computer-aided colonic polyp detector, since a human reader and a computer-aided polyp detector may have optimal performances at different z resolutions. We ran a computer-aided polyp detection algorithm on phantom data sets as well as data obtained from a single patient. All data were reconstructed at various slice thicknesses ranging from 1.25 to 10 mm. We studied the performance of the detector at various ranges of polyp sizes using free-response receiver-operating characteristic analyses. We also studied contrast-to-noise ratios (CNR) as a function of slice thickness and polyp size. For the phantom data, reducing the slice thickness from 5 to 1.25 mm improves sensitivity from 84.5% to 98.3% (all polyps), from 61.4% to 95.5% (polyps in the range [0, 5) mm) and from 97.7% to 100% (polyps in the range [5, 10) mm) at a false positive rate of 20 per data set. For polyps larger than 10 mm, there is no significant improvement in detection sensitivity when slice thickness is reduced. CNRs showed expected behavior with slice thickness and polyp size, but in all cases remained high (> 4). The results for the patient data followed similar patterns to that of the phantom case. Thus we conclude that for this detector, the optimal slice thickness is dependent upon the size of the smallest polyps to be detected. For detection of polyps 10 mm and larger, reconstruction of 5 mm sections may be sufficient. Further study is required to generalize these results to a broader population of patients scanned on different scanners.     

Automatic polyp detection and measurement with computed tomographic colonography: A phantom study

Purpose

The purpose of this study is to assess the performance of computer-aided detection (CAD) software in detecting and measuring polyps for CT Colonography, based on an in vitro phantom study.

Material and methods

A colon phantom was constructed with a PVC pipe of 3.8 cm diameter. Nine simulated polyps of various sizes (3.2mm-25.4mm) were affixed inside the phantom that was placed in a water bath. The phantom was scanned on a 64-slice CT scanner with tube voltage of 120 kV and current of 205 mAs. Two separate scans were performed, with different slice thickness and reconstruction interval. The first scan (thin) had a slice thickness of 1mm and reconstruction interval 0.5mm. The second scan (thick) had a slice thickness of 2mm and reconstruction interval of 1mm. Images from both scans were processed using CT Colonography software that automatically segments the colon phantom and applies CAD that automatically highlights and provides the size (maximum and minimum diameters, volume) of each polyp. Two readers independently measured each polyp (two orthogonal diameters) using both 2D and 3D views. Readers’ manual measurements (diameters) and automatic measurements from CAD (diameters and volume) were compared to actual polyp sizes as measured by mechanical calipers.

Results

All polyps except the smallest (3.2mm) were detected by CAD. CAD achieved 100% sensitivity in detecting polyps ≥6mm. Mean errors in CAD automated volume measurements for thin and thick slice scans were 8.7% and 6.8%, respectively. Almost all CAD and manual readers’ 3D measurements overestimated the size of polyps to variable extent. Both over- and underestimation of polyp sizes were observed in the readers’ manual 2D measurements. Overall, Reader 1 (expert) had smaller mean error than Reader 2 (non-expert).

Conclusion

CAD provided accurate size measurements for all polyps, and results were comparable to the two readers'' manual measurements     

Reduction of false positives on the rectal tube in computer-aided detection for CT colonography

PURPOSE: To eliminate false-positive (FP) polyp detections on the rectal tube (RT) in CT colonography (CTC) computer-aided detection (CAD). METHODS: We use a three-stage approach to detect the RT: detect the RT shaft, track the tube to the tip and label all the voxels that belong to the RT. We applied our RT detection algorithm on a CTC dataset consisting of 80 datasets (40 patients scanned in both prone and supine positions). Two different types of RTs were present, characterized by differences in shaft/bulb diameters, wall intensities, and shape of tip. RESULTS: The algorithm detected 90% of RT shafts and completely tracked 72% of them. We labeled all the voxels belonging to the completely tracked RTs (72%) and in 11 out of 80 (14%) cases the RT voxels were partially labeled. We obtained a 9.2% reduction of the FPs in the initial polyp candidates' population, and a 7.9% reduction of the FPs generated by our CAD system. None of the true-positive detections were mislabeled. CONCLUSIONS: The algorithm detects the RTs with good accuracy, is robust with respect to the two different types of RT used in our study, and is effective at reducing the number of RT FPs reported by our CAD system.     

Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography

We evaluated the effect of our novel technique of feature-guided analysis of polyps on the reduction of false-positive (FP) findings generated by our computer-aided diagnosis (CAD) scheme for the detection of polyps from computed tomography colonographic data sets. The detection performance obtained by use of feature-guided analysis in the segmentation and feature analysis of polyp candidates was compared with that obtained by use of our previously employed fuzzy clustering technique. We also evaluated the effect of a feature called modified gradient concentration (MGC) on the detection performance. A total of 144 data sets, representing prone and supine views of 72 patients that included 14 patients with 21 colorectal polyps 5-25 mm in diameter, were used in the evaluation. At a 100% by-patient (95% by-polyp) detection sensitivity, the FP rate of our CAD scheme with feature-guided analysis based on round-robin evaluation was 1.3 (1.5) FP detections per patient. This corresponds to a 70-75% reduction in the number of FPs obtained by use of fuzzy clustering at the same sensitivity levels. Application of the MGC feature instead of our previously used gradient concentration feature did not improve the detection result. The results indicate that feature-guided analysis is useful for achieving high sensitivity and a low FP rate in our CAD scheme.     

On the comparison of FROC curves in mammography CAD systems

We present a novel method for assessing the performance of computer-aided detection systems on unseen cases at a given sensitivity level. The sampling error introduced when training the system on a limited data set is captured as the uncertainty in determining the system threshold that would yield a certain predetermined sensitivity on unseen data sets. By estimating the distribution of system thresholds, we construct a confidence interval for the expected number of false positive markings per image at a given sensitivity. We present two alternative procedures for estimating the probability density functions needed for the construction of the confidence interval. The first is based on the common assumption of Poisson distributed number of false positive markings per image. This procedure also relies on the assumption of independence between false positives and sensitivity, an assumption that can be relaxed with the second procedure, which is nonparametric. The second procedure uses the bootstrap applied to the data generated in the leave-one-out construction of the FROC curve, and is a fast and robust way of obtaining the desired confidence interval. Standard FROC curve analysis does not account for the uncertainty in setting the system threshold, so this method should allow for a more fair comparison of different systems. The resulting confidence intervals are surprisingly wide. For our system a conventional FROC curve analysis yields 0.47 false positive markings per image at 90% sensitivity. The 90% confidence interval for the number of false positive markings per image is (0.28, 1.02) with the parametric procedure and (0.27, 1.04) with the nonparametric bootstrap. Due to its computational simplicity and its allowing more fair comparisons between systems, we propose this method as a complement to the traditionally presented FROC curves.     

Categorical variables, interactions and generalized additive models. Applications in computer-aided diagnosis systems

Recently, the generalized additive models (GAMs) have been presented as a novel statistical approach to distinguish lesion/non-lesion in computer-aided diagnosis (CAD) systems. In this paper, we present an extension of the GAM that allows for the introduction of factors and their interactions with continuous variables, for reducing false positives in a CAD system for detecting clustered microcalcifications in digital mammograms. The results obtained have shown an increase in the sensitivity from 83.12% to 85.71%, while the false positive rate was drastically reduced from 1.46 to 0.74 false detections per image.     

Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique.

We have been developing a computer-aided diagnostic (CAD) scheme to assist radiologists in improving the detection of pulmonary nodules in chest radiographs, because radiologists can miss as many as 30% of pulmonary nodules in routine clinical practice. A key to the successful clinical application of a CAD scheme is to ensure that there are only a small number of false positives that are incorrectly reported as nodules by the scheme. In order to significantly reduce the number of false positives in our CAD scheme, we developed, in this study, a multiple-template matching technique, in which a test candidate can be identified as a false positive and thus eliminated, if its largest cross-correlation value with non-nodule templates is larger than that with nodule templates. We describe the technique for determination of cross-correlation values for test candidates with nodule templates and non-nodule templates, the technique for creation of a large number of nodule templates and non-nodule templates, and the technique for removal of nodulelike non-nodule templates and non-nodulelike nodule templates, in order to achieve a good performance. In our study, a large number of false positives (44.3%) were removed with reduction of a very small number of true positives (2.3%) by use of the multiple-template matching technique. We believe that this technique can be used to significantly improve the performance of CAD schemes for lung nodule detection in chest radiographs.     

Computer-Aided Detection of Intracranial Aneurysms in MR Angiography

Intracranial aneurysms represent a significant cause of morbidity and mortality. While the risk factors for aneurysm formation are known, the detection of aneurysms remains challenging. Magnetic resonance angiography (MRA) has recently emerged as a useful non-invasive method for aneurysm detection. However, even for experienced neuroradiologists, the sensitivity to small (<5 mm) aneurysms in MRA images is poor, on the order of 30~60% in recent, large series. We describe a fully automated computer-aided detection (CAD) scheme for detecting aneurysms on 3D time-of-flight (TOF) MRA images. The scheme locates points of interest (POIs) on individual MRA datasets by combining two complementary techniques. The first technique segments the intracranial arteries automatically and finds POIs from the segmented vessels. The second technique identifies POIs directly from the raw, unsegmented image dataset. This latter technique is useful in cases of incomplete segmentation. Following a series of feature calculations, a small fraction of POIs are retained as candidate aneurysms from the collected POIs according to predetermined rules. The CAD scheme was evaluated on 287 datasets containing 147 aneurysms that were verified with digital subtraction angiography, the accepted standard of reference for aneurysm detection. For two different operating points, the CAD scheme achieved a sensitivity of 80% (71% for aneurysms less than 5 mm) with three mean false positives per case, and 95% (91% for aneurysms less than 5 mm) with nine mean false positives per case. In conclusion, the CAD scheme showed good accuracy and may have application in improving the sensitivity of aneurysm detection on MR images.     

Techniques of CT colonography (virtual colonoscopy)

Colorectal cancer is a leading cause of death in older adults, which usually involves a long-term progressive change of normal mucosa into adenomatous polyps and then cancer. The detection and treatment of this disease in an early stage can lead to a cure in most cases by simply removing the polyp. Computed tomographic colonography (CTC), also referred to as virtual colonoscopy (VC), is a recent advance that gives an intraluminal visualization of the colon that is similar to endoscopy. VC requires fast 3D display (at least 10 frames/sec) of the colon's mucosal surface on a computer screen. Spiral/helical computer tomography is used to gather 3D volume data prior to display. CTC has been demonstrated to be promising for colorectal cancer screening. Studies on unraveling of the colon are underway to map the convoluted tubular structure into a straightened and flattened image volume for global visualization. In this article, we review the current status of CTC with an emphasis on image processing and visualization algorithms. Clinical assessment results of existing techniques are summarized. Practical issues and future perspectives are also discussed.     

Adaptive deformable model for colonic polyp segmentation and measurement on CT colonography

Polyp size is one important biomarker for the malignancy risk of a polyp. This paper presents an improved approach for colonic polyp segmentation and measurement on CT colonography images. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy clustering, and adaptive deformable model. Since polyps on haustral folds are the most difficult to be segmented, we propose a dual-distance algorithm to first identify voxels on the folds, and then introduce a counter-force to control the model evolution. We derive linear and volumetric measurements from the segmentation. The experiment was conducted on 395 patients with 83 polyps, of which 43 polyps were on haustral folds. The results were validated against manual measurement from the optical colonoscopy and the CT colonography. The paired t-test showed no significant difference, and the R(2) correlation was 0.61 for the linear measurement and 0.98 for the volumetric measurement. The mean Dice coefficient for volume overlap between automatic and manual segmentation was 0.752 (standard deviation 0.154).     

Development and evaluation of a robust algorithm for computer-assisted detection of sentinel lymph node micrometastases

Clarke G M, Peressotti C, Holloway C M B, Zubovits J T, Liu K & Yaffe M J
(2011) Histopathology 59 , 116–128 Development and evaluation of a robust algorithm for computer‐assisted detection of sentinel lymph node micrometastases Aims: Increasing the sectioning rate for breast sentinel lymph nodes can increase the likelihood of detecting micrometastases. To make serial sectioning feasible, we have developed an algorithm for computer‐assisted detection (CAD) with digitized lymph node sections. Methods and results: K‐means clustering assigned image pixels to one of four areas in a colourspace (representing tumour, unstained background, counterstained background and microtomy artefacts). Four filters then removed ‘false‐positive’ pixels from the tumour cluster. A set of 43 sections containing tumour (a total of 259 foci) and 59 sections negative for malignancy was defined by two pathologists, using light microscopy, and CAD was applied. For the clinically relevant task of identifying the largest focus in each section (micrometastasis in 22/43 sections), the sensitivity and specificity were 100%. Isolated tumour cells (ITCs) were identified in one slide initially considered to be negative. Identification of all 259 foci yielded sensitivities of 57.5% for ITCs (<0.200 mm), 89.5% for micrometastases, and 100% for larger metastases, with one false‐positive. Reduced sensitivity was ascribed to variable staining. Nine additional metastases (<0.01–0.3 mm) that were not initially identified were detected by CAD. Conclusions: This algorithm is well suited to the task of sentinel lymph node evaluation and may enhance the detection of occult micrometastases.