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Digital Mammography, 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings; 01/2006
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ABSTRACT: To retrospectively evaluate the effect of computer-aided detection (CAD) on radiologists' performance in detection of intracranial aneurysms with magnetic resonance (MR) angiography.
The institutional review board approved this study and did not require patient informed consent. Fifty maximum intensity projection MR angiograms in 50 patients were used for observer performance study. The group included 22 patients (age range, 43-86 years; mean, 60.2 years; 6 men and 16 women) with intracranial aneurysms and 28 patients (age range, 32-80 years; mean, 58.8 years; 10 men and 18 women) without aneurysms. The MR angiograms were obtained with three-dimensional time-of-flight 1.5-T MR imaging. Fifteen radiologists, including eight neuroradiologists and seven general radiologists, participated in the observer performance test. They interpreted the angiograms first without and then with the aid of the computer output by using an automated computerized scheme. The observers' performance without and with the computer output was evaluated with receiver operating characteristic analysis.
For all 15 observers, average area under the receiver operating characteristic curve (A(z)) value for detection of aneurysms was increased significantly from 0.931 to 0.983 (P = .001) when they used the computer output. A(z) values for general radiologists and neuroradiologists increased from 0.894 to 0.983 (P = .022) and from 0.963 to 0.984 (P = .014), respectively. Improvement in the performance of general radiologists in terms of the A(z) value was much greater than that of neuroradiologists. Performance of general radiologists with CAD (A(z) = 0.983) slightly exceeded that of neuroradiologists without CAD (A(z) = 0.963) (P = .048).
CAD improved neuroradiologists' and general radiologists' performance for detection of intracranial aneurysms with MR angiography; improvement was greater for general radiologists than it was for neuroradiologists.
Radiology 12/2005; 237(2):605-10. · 5.73 Impact Factor
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ABSTRACT: Computer-aided diagnosis (CAD) provides a computerized diagnostic result as a "second opinion" to assist radiologists in the diagnosis of various diseases by use of medical images. CAD has become a practical clinical approach in diagnostic radiology, although, at present, primarily in the area of detection of breast cancer in mammograms. Currently, a large research effort has been devoted to the detection and classification of various lung diseases in thoracic computed tomography (CT) images. We describe in this article the current status of the development of CAD schemes in thoracic CT, including nodule detection, distinction between benign and malignant nodules, and detection, characterization, and differential diagnosis of diffuse lung disease. Observer performance studies indicate that these CAD schemes would be useful in clinical practice by providing radiologists with computer output as a "second opinion."
Seminars in Ultrasound CT and MRI 11/2005; 26(5):357-63. · 1.24 Impact Factor
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ABSTRACT: To retrospectively evaluate whether a difference-image computer-aided detection (CAD) scheme can help radiologists detect peripheral lung cancers missed at low-dose computed tomography (CT).
Institutional review board approval and informed patient and observer consent were obtained. Seventeen patients (eight men and nine women; mean age, 60 years) with a missed peripheral lung cancer and 10 control subjects (five men and five women; mean age, 63 years) without cancer at low-dose CT were included in an observer study. Fourteen radiologists were divided into two groups on the basis of different image display formats: Six radiologists (group 1) reviewed CT scans with a multiformat display, and eight radiologists (group 2) reviewed images with a "stacked" cine-mode display. The radiologists, first without and then with the CAD scheme, indicated their confidence level regarding the presence (or absence) of cancer and the most likely position of a lesion on each CT scan. Receiver operating characteristic (ROC) curves were calculated without and with localization to evaluate the observers' performance.
With the CAD scheme, the average area under the ROC curve improved from 0.763 to 0.854 for all radiologists (P = .002), from 0.757 to 0.862 for group 1 (P = .04), and from 0.768 to 0.848 for group 2 (P = .01). The average sensitivity in the detection of 17 cancers improved from 52% (124 of 238 observations) to 68% (163 of 238 observations) for all radiologists (P < .001), from 49% (50 of 102 observations) to 71% (72 of 102 observations) for group 1 (P = .02), and from 54% (74 of 136 observations) to 67% (91 of 136 observations) for group 2 (P = .006). The localization ROC curve also improved.
Lung cancers missed at low-dose CT were very difficult to detect, even in an observer study. The use of CAD, however, can improve radiologists' performance in the detection of these subtle cancers.
Radiology 11/2005; 237(2):684-90. · 5.73 Impact Factor
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ABSTRACT: To demonstrate that a massive training artificial neural network (MTANN) can be adequately trained with a small number of cases in the distinction between nodules and vessels (non-nodules) in thoracic computed tomography (CT) images.
An MTANN is a trainable, highly nonlinear filter consisting of a linear-output multilayer artificial neural network model. For enhancement of nodules and suppression of vessels, we used 10 nodules and 10 non-nodule images as training cases for MTANNs. The MTANN is trained with a large number of input subregions selected from the training cases and the corresponding pixels in teaching images that contain Gaussian distributions for nodules and zero for non-nodules. We trained three MTANNs with different numbers (1, 9, and 361) of training samples (pairs of the subregion and the teaching pixel) selected from the training cases. In order to investigate the basic characteristics of the trained MTANNs, we applied the MTANNs to simulated CT images containing various-sized model nodules (spheres) with different contrasts and various-sized model vessels (cylinders) with different orientations. In addition, we applied the trained MTANNs to nontraining actual clinical cases with 59 nodules and 1,726 non-nodules.
In the output images for the simulated CT images by use of the MTANNs trained with small numbers (one and nine) of subregions, model vessels were clearly visible and were not removed; thus, the MTANNs were not trained properly. However, in the output image of the MTANN trained with a large number of subregions, various-sized model nodules with different contrasts were represented by light nodular distributions, whereas various-sized model vessels with different orientations were dark and thus were almost removed. This result indicates that the MTANN was able to learn, from a very small number of actual nodule and non-nodule cases, the distinction between nodules (spherelike objects) and vessels (cylinder-like objects). In nontraining clinical cases, the MTANN was able to distinguish actual nodules from actual vessels in CT images. For 59 actual nodules and 1,726 non-nodules, the performance of the MTANN decreased as the number of training samples (subregions) in each case decreased.
The MTANN can be trained with a very small number of training cases (10 nodules and 10 non-nodules) in the distinction between nodules and non-nodules (vessels) in CT images. Massive training by scanning of training cases to produce a large number of training samples (input subregions and teaching pixels) would contributed to a high generalization ability of the MTANN.
Academic Radiology 11/2005; 12(10):1333-41. · 1.69 Impact Factor
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ABSTRACT: Low-dose helical computed tomography (LDCT) is being applied as a modality for lung cancer screening. It may be difficult, however, for radiologists to distinguish malignant from benign nodules in LDCT. Our purpose in this study was to develop a computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT scans by use of a massive training artificial neural network (MTANN). The MTANN is a trainable, highly nonlinear filter based on an artificial neural network. To distinguish malignant nodules from six different types of benign nodules, we developed multiple MTANNs (multi-MTANN) consisting of six expert MTANNs that are arranged in parallel. Each of the MTANNs was trained by use of input CT images and teaching images containing the estimate of the distribution for the "likelihood of being a malignant nodule," i.e., the teaching image for a malignant nodule contains a two-dimensional Gaussian distribution and that for a benign nodule contains zero. Each MTANN was trained independently with ten typical malignant nodules and ten benign nodules from each of the six types. The outputs of the six MTANNs were combined by use of an integration ANN such that the six types of benign nodules could be distinguished from malignant nodules. After training of the integration ANN, our scheme provided a value related to the "likelihood of malignancy" of a nodule, i.e., a higher value indicates a malignant nodule, and a lower value indicates a benign nodule. Our database consisted of 76 primary lung cancers in 73 patients and 413 benign nodules in 342 patients, which were obtained from a lung cancer screening program on 7847 screenees with LDCT for three years in Nagano, Japan. The performance of our scheme for distinction between benign and malignant nodules was evaluated by use of receiver operating characteristic (ROC) analysis. Our scheme achieved an Az (area under the ROC curve) value of 0.882 in a round-robin test. Our scheme correctly identified 100% (76/76) of malignant nodules as malignant, whereas 48% (200/413) of benign nodules were identified correctly as benign. Therefore, our scheme may be useful in assisting radiologists in the diagnosis of lung nodules in LDCT.
IEEE Transactions on Medical Imaging 10/2005; 24(9):1138-50. · 3.64 Impact Factor
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ABSTRACT: We investigated a psychophysical similarity measure for selection of images similar to those of unknown masses on mammograms, which may assist radiologists in the distinction between benign and malignant masses. Sixty pairs of masses were selected from 1445 mass images prepared for this study, which were obtained from the Digital Database for Screening Mammography by the University of South Florida. Five radiologists provided subjective similarity ratings for these 60 pairs of masses based on the overall impression for diagnosis. Radiologists' subjective ratings were marked on a continuous rating scale and quantified between 0 and 1, which correspond to pairs not similar at all and pairs almost identical, respectively. By use of the subjective ratings as "gold standard," similarity measures based on the Euclidean distance between pairs in feature space and the psychophysical measure were determined. For determination of the psychophysical similarity measure, an artificial neural network (ANN) was employed to learn the relationship between radiologists' average subjective similarity ratings and computer-extracted image features. To evaluate the usefulness of the similarity measures, the agreement with the radiologists' subjective similarity ratings was assessed in terms of correlation coefficients between the average subjective ratings and the similarity measures. A commonly used similarity measure based on the Euclidean distance was moderately correlated (r=0.644) with the radiologists' average subjective ratings, whereas the psychophysical measure by use of the ANN was highly correlated (r=0.798). The preliminary result indicates that a psychophysical similarity measure would be useful in the selection of images similar to those of unknown masses on mammograms.
Medical Physics 08/2005; 32(7):2295-304. · 2.83 Impact Factor
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ABSTRACT: We have developed a computer-aided diagnostic (CAD) scheme for detection of unruptured intracranial aneurysms in magnetic resonance angiography (MRA) based on findings of short branches in vessel skeletons, and a three-dimensional (3D) selective enhancement filter for dots (aneurysms). Fifty-three cases with 61 unruptured aneurysms and 62 non-aneurysm cases were tested in this study. The isotropic 3D MRA images with 400 x 400 x 128 voxels (a voxel size of 0.5 mm) were processed by use of the dot enhancement filter. The initial candidates were identified not only on the dot-enhanced images by use of a multiple gray-level thresholding technique, but also on the vessel skeletons by finding short branches on parent skeletons, which can indicate a high likelihood of small aneurysms. All candidates were classified into four categories of candidates according to effective diameter and local structure of the vessel skeleton. In each category, a number of false positives were removed by use of two rule-based schemes and by linear discriminant analysis on localized image features related to gray level and morphology. Our CAD scheme achieved a sensitivity of 97% with 5.0 false positives per patient by use of a leave-one-out-by-patient test method. This CAD system may be useful in assisting radiologists in the detection of small intracranial aneurysms as well as medium-size aneurysms in MRA.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
04/2005;
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ABSTRACT: The cardio-thoracic ratio (CTR) is commonly measured manually for the evaluation of cardiomegaly. To determine the CTR automatically, we have developed a computerized scheme based on gray-level histogram analysis and an edge detection technique with feature analysis. The database used in this study consisted of 392 chest radiographs, which included 304 normals and 88 abnormals with cardiomegaly. The pixel size and the quantization level of the image were 0.175 mm and 1024, respectively. We performed a nonlinear density correction to maintain consistency in the density and contrast of the image. Initial heart edge detection was performed by selection of a certain range of pixel values in the histogram of a rectangular area at the center of a low-resolution image. Feature analysis with use of an edge gradient and with the orientation obtained by a Sobel operator was applied for accurate identification of the heart edges, which tend to have large edge gradients in a certain range of orientations. In addition, to determine the CTR, we detected the ribcage edges automatically by using image profile analysis. In 94.9% of all of the cases, the heart edges were detected accurately by use of this scheme. The area under the ROC curve (Az value) in distinguishing between normals and abnormals with cardiomegaly based on the CTR was 0.912. Because the CTR is measured automatically and quickly (in less than 1 sec.), radiologists could save reading time. The computerized scheme will be useful for the assessment of cardiomegaly on chest radiographs.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
04/2005;
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ABSTRACT: The purpose of this study was to evaluate the performance of a fully automated lung nodule detection method in a large database of low-dose computed tomography (CT) scans from a lung cancer screening program. Because nodules demonstrate a spectrum of radiologic appearances, the performance of the automated method was evaluated on the basis of nodule malignancy status, size, subtlety, and radiographic opacity.
A database of 393 thick-section (10 mm) low-dose CT scans was collected. Automated lung nodule detection proceeds in two phases: gray-level thresholding for the initial identification of nodule candidates, followed by the application of a rule-based classifier and linear discriminant analysis to distinguish between candidates that correspond to actual lung nodules and candidates that correspond to non-nodules. Free-response receiver operating characteristic analysis was used to evaluate the performance of the method based on a jackknife training/testing approach.
An overall nodule detection sensitivity of 70% (330 of 470) was attained with an average of 1.6 false-positive detections per section. At the same false-positive rate, 83% (57 of 69) of the malignant lung nodules in the database were detected. When the method was trained specifically for malignant nodules, a sensitivity of 80% (55 of 69) was attained with 0.85 false-positives per section.
We have evaluated an automated lung nodule detection method with a large number of low-dose CT scans from a lung cancer screening program. An overall sensitivity of 80% for malignant nodules was achieved with 0.85 false-positive detections per section. Such a computerized lung nodule detection method is expected to become an important part of CT-based lung cancer screening programs.
Academic Radiology 04/2005; 12(3):337-46. · 1.69 Impact Factor
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ABSTRACT: We developed a technique that uses a multiple massive-training artificial neural network (multi-MTANN) to reduce the number of false-positive results in a computer-aided diagnostic (CAD) scheme for detecting nodules in chest radiographs.
Our database consisted of 91 solitary pulmonary nodules, including 64 malignant nodules and 27 benign nodules, in 91 chest radiographs. With our current CAD scheme based on a difference-image technique and linear discriminant analysis, we achieved a sensitivity of 82.4%, with 4.5 false positives per image. We developed the multi-MTANN for further reduction of the false positive rate. An MTANN is a highly nonlinear filter that can be trained with input images and corresponding teaching images. To reduce the effects of background levels in chest radiographs, we applied a background-trend-correction technique, followed by contrast normalization, to the input images for the MTANN. For enhancement of nodules, the teaching image was designed to contain the distribution for a "likelihood of being a nodule." Six MTANNs in the multi-MTANN were trained by using typical nodules and six different types of non-nodules (false positives).
Use of the trained multi-MTANN eliminated 68.3% of false-positive findings with a reduction of one true-positive result. The false-positive rate of our original CAD scheme was improved from 4.5 to 1.4 false positives per image, at an overall sensitivity of 81.3%.
Use of a multi-MTANN substantially reduced the false-positive rate of our CAD scheme for lung nodule detection on chest radiographs, while maintaining a level of sensitivity.
Academic Radiology 03/2005; 12(2):191-201. · 1.69 Impact Factor
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ABSTRACT: The aim of the study was to survey misfiled cases in a picture archiving and communication system environment at two hospitals and to demonstrate the potential usefulness of an automated patient recognition method for posteroanterior chest radiographs based on a template-matching technique designed to prevent filing errors.
We surveyed misfiled cases obtained from different modalities in one hospital for 25 months, and misfiled cases of chest radiographs in another hospital for 17 months. For investigating the usefulness of an automated patient recognition and identification method for chest radiographs, a prospective study has been completed in clinical settings at the latter hospital.
The total numbers of misfiled cases for different modalities in one hospital and for chest radiographs in another hospital were 327 and 22, respectively. The misfiled cases in the two hospitals were mainly the result of human errors (eg, incorrect manual entries of patient information, incorrect usage of identification cards in which an identification card for the previous patient was used for the next patient's image acquisition). The prospective study indicated the usefulness of the computerized method for discovering misfiled cases with a high performance (ie, an 86.4% correct warning rate for different patients and 1.5% incorrect warning rate for the same patients).
We confirmed the occurrence of misfiled cases in the two hospitals. The automated patient recognition and identification method for chest radiographs would be useful in preventing wrong images from being stored in the picture archiving and communication system environment.
Academic Radiology 02/2005; 12(1):97-103. · 1.69 Impact Factor
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ABSTRACT: To evaluate thin-section computed tomographic (CT) characteristics of malignant nodules on the basis of overall appearance (pure ground-glass opacity [GGO], mixed GGO, or solid opacity) in comparison with the appearance of benign nodules.
Institutional review board approval and patient consent were obtained. Follow-up diagnostic CT was performed in 747 suspicious pulmonary nodules detected at low-dose CT screening (17 892 examinations). Of 747 nodules, 222 were evaluated at thin-section CT (1-mm collimation), which included 59 cancers and 163 benign nodules (3-20 mm). Thin-section CT findings of malignant versus benign nodules with pure GGO (17 vs 12 lesions), mixed GGO (27 vs 29 lesions), or solid opacity (15 vs 122 lesions) were analyzed. Fisher exact test for independence was used to compare differences in shape, margin, and internal features between benign and malignant nodules. Positive predictive value (PPV) was analyzed when a category was significantly different from the others.
Among nodules with pure GGO, a round shape was found more frequently in malignant lesions (11 of 17, 65%) than in benign lesions (two of 12, 17%; P = .02; PPV, 85%); mixed GGO, a subtype with GGO in the periphery and a high-attenuation zone in the center, was seen much more often in malignant lesions (11 of 27, 41%) than in benign lesions (two of 29, 7%; P = .004; PPV, 85%). Among solid nodules, a polygonal shape or a smooth or somewhat smooth margin was present less frequently in malignant than in benign lesions (polygonal shape: 7% vs 38%, P = .02; smooth or somewhat smooth margin: 0% vs 63%, P < .001), and 98% (46 of 47) of polygonal nodules and 100% (77 of 77) of nodules with a smooth or somewhat smooth margin were benign.
Recognition of certain characteristics at thin-section CT can be helpful in differentiating small malignant nodules from benign nodules.
Radiology 01/2005; 233(3):793-8. · 5.73 Impact Factor
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ABSTRACT: To evaluate the effect of temporal subtraction images on the radiologists' detection of early primary lung cancer in computed tomography (CT) scans.
Fourteen cases with primary lung cancer and 16 normal cases were used for this study from a database of low-dose CT images, which were obtained from a lung cancer screening program in Nagano, Japan. Images were obtained with a single-detector helical CT scanner using 10 mm collimation and 2:1 pitch. Each case had both previous and current CT scans. Temporal subtraction images were obtained by subtracting the warped previous images from the current images. Seven radiologists, including four attendings and three residents, provided their confidence levels for the presence or absence of lung cancers with use of film CT images without and with temporal subtraction images. Receiver operating characteristic analysis was used to compare their performance without and with temporal subtraction images.
The mean Az values (area under the receiver operating characteristic curve) of seven observers without and with temporal subtraction images were 0.868 and 0.930, respectively. Diagnostic accuracy was significantly improved by using temporal subtraction images (P = .007). Temporal subtraction images were especially useful when a nodule was present near the pulmonary hilum, where radiologists tended to overlook it.
The temporal subtraction technique can significantly improve the sensitivity and specificity for detection of lung cancer on CT scans.
Academic Radiology 01/2005; 11(12):1337-43. · 1.69 Impact Factor
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ABSTRACT: Rationale and Objective. We developed a technique that uses a multiple massive-training artificial neural network (multi- MTANN) to reduce the number of false-positive results in a computer-aided diagnostic (CAD) scheme for detecting nod- ules in chest radiographs. Materials and Methods. Our database consisted of 91 solitary pulmonary nodules, including 64 malignant nodules and 27 benign nodules, in 91 chest radiographs. With our current CAD scheme based on a difference-image technique and linear discriminant analysis, we achieved a sensitivity of 82.4%, with 4.5 false positives per image. We developed the multi-MTANN for further reduction of the false positive rate. An MTANN is a highly nonlinear filter that can be trained with input images and corresponding teaching images. To reduce the effects of background levels in chest radiographs, we applied a background-trend-correction technique, followed by contrast normalization, to the input images for the MTANN. For enhancement of nodules, the teaching image was designed to contain the distribution for a "likelihood of being a nod- ule." Six MTANNs in the multi-MTANN were trained by using typical nodules and six different types of non-nodules (false positives). Results. Use of the trained multi-MTANN eliminated 68.3% of false-positive findings with a reduction of one true- positive result. The false-positive rate of our original CAD scheme was improved from 4.5 to 1.4 false positives per image, at an overall sensitivity of 81.3%. Conclusion. Use of a multi-MTANN substantially reduced the false-positive rate of our CAD scheme for lung nodule de- tection on chest radiographs, while maintaining a level of sensitivity. AUR, 2005
Academic Radiology - ACAD RADIOL. 01/2005; 12(2):191-201.
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ABSTRACT: The purpose of our study was to evaluate whether a computer-aided diagnosis (CAD) scheme can assist radiologists in distinguishing small benign from malignant lung nodules on high-resolution CT (HRCT).
We developed an automated computerized scheme for determining the likelihood of malignancy of lung nodules on multiple HRCT slices; the likelihood estimate was obtained from various objective features of the nodules using linear discriminant analysis. The data set used in this observer study consisted of 28 primary lung cancers (6-20 mm) and 28 benign nodules. Cancer cases included nodules with pure ground-glass opacity, mixed ground-glass opacity, and solid opacity. Benign nodules were selected by matching their size and pattern to the malignant nodules. Consecutive region-of-interest images for each nodule on HRCT were displayed for interpretation in stacked mode on a cathode ray tube monitor. The images were presented to 16 radiologists-first without and then with the computer output-who were asked to indicate their confidence level regarding the malignancy of a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis.
The area under the ROC curve (Az value) of the CAD scheme alone was 0.831 for distinguishing benign from malignant nodules. The average Az value for radiologists was improved with the aid of the CAD scheme from 0.785 to 0.853 by a statistically significant level (p = 0.016). The radiologists' diagnostic performance with the CAD scheme was more accurate than that of the CAD scheme alone (p < 0.05) and also that of radiologists alone.
CAD has the potential to improve radiologists' diagnostic accuracy in distinguishing small benign nodules from malignant ones on HRCT.
American Journal of Roentgenology 12/2004; 183(5):1209-15. · 2.78 Impact Factor
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ABSTRACT: A computerized scheme for automated detection of unruptured intracranial aneurysms in magnetic resonance angiography was developed based on the use of a three-dimensional selective enhancement filter for dots (aneurysms).
Twenty-nine cases with 36 unruptured aneurysms (diameter, 3 to 26 mm; mean, 6.6 mm) and 31 non-aneurysm cases were used in this study. The isotropic 3-dimensional magnetic resonance angiography images with 400 x 400 x 128 voxels (voxel size, 0.5 mm) were processed by use of the selective enhancement filter. The initial candidates were identified by use of a multiple gray-level thresholding technique on the dot-enhanced images and a region-growing technique with monitoring some image features. All candidates were classified into four types of candidates according to the size and local structures based on the effective diameter and skeleton image of each candidate (ie, large candidates and three types of small candidates including short-branch type, single-vessel type, and bifurcation type). In each group, a number of false-positives were removed by use of different rules on localized image features related to gray levels and morphology. Linear discriminant analysis was used for further removal of false-positives.
With this computer-aided diagnostic scheme, all of 36 aneurysms were correctly detected with 2.4 false-positives per patient based on a leave-one-out-by-patient test method.
This computer-aided diagnostic system would be useful in assisting radiologists for the detection of intracranial aneurysms in magnetic resonance angiography.
Academic Radiology 11/2004; 11(10):1093-104. · 1.69 Impact Factor
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Kunio Doi
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ABSTRACT: Computer-aided diagnosis (CAD) has become a practical clinical approach in diagnostic radiology, although at present only in the area of detection of breast cancer in mammograms. Current research efforts have been focused on detection and classification of images of many different types of lesions in a number of organs, obtained with various imaging modalities. It is likely that the present results of CAD are only at the tip of the iceberg. Although automated computer diagnosis is a concept based on computer algorithms only, CAD is a concept established by taking into account equally the roles of physicians and computers. The effect of CAD on differential diagnosis has already indicated that the performance level is high, and that CAD would be ready for clinical trials and commercialization efforts. The presentation of images similar to those of an unknown case may be useful as a supplemental tool for CAD in the differential diagnosis.
Seminars in Ultrasound CT and MRI 11/2004; 25(5):404-10. · 1.24 Impact Factor
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ABSTRACT: Chest radiography is still a useful examination in various situations, although CT has become a modality of choice as a diagnostic examination in many cases. Current computer-aided diagnosis (CAD) schemes for chest radiographs include nodule detection, interstitial disease detection, temporal subtraction, differential diagnosis of interstitial disease, and distinction between benign and malignant pulmonary nodules. All of these schemes are demonstrated as providing potentially useful tools for radiologists when the output of these schemes is used as a "second opinion." There are some commercially available products for these schemes and more are expected to be available in the near future. The current status of CAD for CT is also discussed briefly in this article.
Seminars in Ultrasound CT and MRI 11/2004; 25(5):432-7. · 1.24 Impact Factor
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Aya Fukushima,
Kazuto Ashizawa,
Tetsuji Yamaguchi,
Naohiro Matsuyama,
Hideyuki Hayashi,
Isao Kida,
Yoshihiro Imafuku,
Akiko Egawa,
Seigo Kimura,
Kenji Nagaoki,
Sumihisa Honda,
Shigehiko Katsuragawa, Kunio Doi,
Kuniaki Hayashi
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ABSTRACT: The purpose of our study was to evaluate the diagnostic performance of an artificial neural network (ANN) in differentiating among certain diffuse lung diseases using high-resolution CT (HRCT) and the effect of ANN output on radiologists' diagnostic performance.
We selected 130 clinical cases of diffuse lung disease. We used a single three-layer, feed-forward ANN with a back-propagation algorithm. The ANN was designed to differentiate among 11 diffuse lung diseases by using 10 clinical parameters and 23 HRCT features. Therefore, the ANN consisted of 33 input units and 11 output units. Subjective ratings for 23 HRCT features were provided independently by eight radiologists. All clinical cases were used for training and testing of the ANN by implementing a round-robin technique. In the observer test, a subset of 45 cases was selected from the database of 130 cases. HRCT images were viewed by eight radiologists first without and then with ANN output. The radiologists' performance was evaluated with receiver operating characteristic (ROC) analysis with a continuous rating scale.
The average area under the ROC curve for ANN performance obtained with all clinical parameters and HRCT features was 0.956. The diagnostic performance of four chest radiologists and four general radiologists was increased from 0.986 to 0.992 (p = 0.071) and 0.958 and 0.971 (p < 0.001), respectively, when they used the ANN output based on their own feature ratings.
The ANN can provide a useful output as a second opinion to improve general radiologists' diagnostic performance in the differential diagnosis of certain diffuse lung diseases using HRCT.
American Journal of Roentgenology 09/2004; 183(2):297-305. · 2.78 Impact Factor
