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Biomedical subjects

Shigehiko Katsuragawa

Publications and source records attributed to Shigehiko Katsuragawa.

At least 19 recordsLinked to original sources
PubMed2006

Automated hepatic volumetry for living related liver transplantation at multisection CT.

PURPOSE: To prospectively compare in vivo hepatic automated volumetry with manual volumetry and measured liver volume. MATERIALS AND METHODS: The study was conducted in accordance with the guidelines of the Institutional Review Board of Kumamoto University (Japan). Patient informed consent was obtained. Preoperative multisection computed tomography (CT) was performed in 35 consecutive patients (21 men, 14 women; mean age, 42.8 years; range, 28-72 years) with hepatic disease awaiting living related liver transplantation. The CT scans covered the entire liver at a section thickness of 2.5 mm. Liver volume was estimated by using both the automated and the manual methods. Actual liver weight was obtained for all patients and was converted to hepatic volume on the basis of a predetermined relationship between actual liver weight and volume. Processing time required for both methods was also recorded. Two-tailed paired t test, correlation coefficient, and Bland-Altman tests were used for statistical analyses. RESULTS: Mean liver weight was 881.7 g +/- 249.8 (standard deviation), and mean measured liver volume was 956.00 cm(3) +/- 280.10. Volumetry performed with the automated and manual methods provided liver volumes of 982.99 cm(3) +/- 301.98 and 937.10 cm(3) +/- 301.31, respectively. There was good correlation between measured and estimated volumes obtained with the automated method (r = 0.792, P < .01). The manual and automated methods required 32.8 minutes +/- 6.9 and 4.4 minutes +/- 1.9, respectively. CONCLUSION: The automated method reduced the time required for volumetry of the liver and provided acceptable measurements.

Adult↗
PubMed2006

[Comparison of LCD and CRT monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic analysis].

Soft copy reading of digital images has been practiced commonly in the PACS environment. In this study, we compared liquid-crystal display (LCD) and cathode-ray tube (CRT) monitors for detection of pulmonary nodules and interstitial lung diseases on digital chest radiographs by using receiver operating characteristic (ROC) analysis. Digital chest images with a 1000x1000 matrix size and a 8 bit grayscale were displayed on LCD/CRT monitor with 2M pixels in each observer test. Eight and ten radiologists participated in the observer tests for detection of nodules and interstitial diseases, respectively. In each observer test, radiologists marked their confidence levels for diagnosis of pulmonary nodules or interstitial diseases. The detection performance of radiologists was evaluated by ROC analyses. The average Az values (area under the ROC curve) in detecting pulmonary nodules with LCD and CRT monitors were 0.792 and 0.814, respectively. In addition, the average Az values in detecting interstitial diseases with LCD and CRT monitors were 0.951 and 0.953, respectively. There was no statistically significant difference between LCD and CRT for both detection of pulmonary nodules (P=0.522) and interstitial lung diseases (P=0.869). Therefore, we believe that the LCD monitor instead of the CRT monitor can be used for the diagnosis of pulmonary nodules and interstitial lung diseases in digital chest images.

Data Display↗
PubMed2006

Usefulness of artificial neural network for differential diagnosis of hepatic masses on CT images.

RATIONALE AND OBJECTIVE: Our purpose in this study is to apply an artificial neural network (ANN) for differential diagnosis of certain hepatic masses on computed tomographic (CT) images and evaluate the effect of ANN output on radiologist diagnostic performance. MATERIALS AND METHODS: We collected 120 cases of hepatic disease. We used a single three-layer feed-forward ANN with a back-propagation algorithm. The ANN is designed to differentiate four hepatic masses (hepatocellular carcinoma, intrahepatic peripheral cholangiocarcinoma, hemangioma, and metastasis) by using nine clinical parameters and 24 radiological findings in dual-phase contrast-enhanced CT images. Thus, the ANN consisted of 33 input units and four output units. Subjective ratings for the 24 radiological findings were provided independently by two attending radiologists. All clinical cases were used for training and testing of the ANN by implementation of a round-robin technique. In the observer test, CT images of all 120 cases (30 cases for each disease) were used. CT images were viewed by seven radiologists first without and then with ANN output. Radiologist performance was evaluated by using receiver operating characteristic (ROC) analysis on a continuous rating scale. RESULTS: Averaged area under the ROC curve for ANN alone was 0.961. The diagnostic performance of seven radiologists increased from 0.888 to 0.934 (P < .02) when they used ANN output. CONCLUSION: The ANN can provide useful output as a second opinion to improve radiologist diagnostic performance in the differential diagnosis of hepatic masses seen on contrast-enhanced CT.

Aged, 80 and over↗
PubMed2006

Computer-aided diagnosis scheme for identifying histological classification of clustered microcalcifications by use of follow-up magnification mammograms.

RATIONALE AND OBJECTIVES: Our purpose in this study was to investigate the usefulness of follow-up magnification mammograms (i.e., both current and previous magnification mammograms) in a computer-aided diagnosis (CAD) scheme for identifying the histological classification of clustered microcalcifications. MATERIALS AND METHODS: Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after 3-month follow-up: 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 noninvasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. In our CAD scheme, we extracted five objective features of clustered microcalcifications from each of the current and previous magnification mammograms by taking into account image features that experienced radiologists commonly use to identify histological classifications. These features were then merged by a modified Bayes discriminant function for distinguishing among five histological classifications. For the input of the modified Bayes discriminant function, we used five objective features obtained from the previous magnification mammogram (previous features), five objective features obtained from the current magnification mammogram (current features), and the set of the five previous features and the five current features. RESULTS: The classification accuracies with the five current features were higher than those with the five previous features. These classification accuracies were improved substantially by using the set of the five previous features and the five current features. For the set of the five previous features and the five current features, the classification accuracies of our CAD scheme were 81.8% (9 of 11) for invasive carcinoma, 84.2% (16 of 19) for noninvasive carcinoma of the comedo type, 76.0% (19 of 25) for noninvasive carcinoma of the noncomedo type, 73.9% (17 of 23) for mastopathy, and 86.8% (13 of 15) for fibroadenoma. CONCLUSION: Our CAD scheme with use of follow-up magnification mammograms improved classification performance for mammographic clustered microcalcifications.

Algorithms↗
PubMed2006

Computerized detection of intracranial aneurysms for three-dimensional MR angiography: feature extraction of small protrusions based on a shape-based difference image technique.

We have improved a computerized scheme for the detection of intracranial aneurysms for three-dimensional (3-D) magnetic resonance angiography (MRA) by the use of image features of small protrusions extracted based on a shape-based difference image (SBDI) technique. Initial candidates were identified by use of a multiple gray-level thresholding technique in dot enhanced images, and by finding short branches in skeleton images. Image features related to aneurysms were determined based on candidate regions segmented by use of a region growing technique. For extracting additional features on small protrusions or small aneurysms, we have developed an SBDI technique, which was based on the shape-based difference between an original segmented vessel and a vessel with suppressed local change in thickness. The SBDI technique was useful for obtaining local changes in vessel thickness, i.e., SBD regions, which could be small aneurysms in the case of true positives, but thin or very small regions in the case of false positives. Many false positives were removed by means of rule-based schemes and linear discriminant analysis on various 3-D localized image features, including SBDI features. We tested the computerized scheme on 53 cases with 61 aneurysms and 62 nonaneurysm cases based on a leave-one-out-by-patient test method. As a result, false positives per patient decreased from 5.8 to 3.8, while a high sensitivity of 97% was maintained by use of the SBDI technique, in which SBDI features were effective for removing some false positives. The computer-aided diagnostic (CAD) scheme may be robust and useful in assisting radiologists in the detection of intracranial aneurysms for MRA.

False Positive Reactions↗
PubMed2006

Effect of temporal subtraction technique on interpretation time and diagnostic accuracy of chest radiography.

OBJECTIVE: Our purpose was to compare reviewing time and diagnostic accuracy in the interpretation of radiographs without and with subtraction images and to examine whether this temporal subtraction technique can contribute to improving radiologists' performance. MATERIALS AND METHODS: Thirty cases with newly developed chest abnormalities on chest radiographs and 90 negative cases were selected. All chest radiographs were obtained with a computed radiography system. For the 90 negative cases, subtraction images were classified into two groups: 33 clean images without misregistration artifacts and 57 images with some misregistration artifacts. Eight radiologists (four board-certified radiologists and four radiology residents) participated in observer tests and interpreted the original radiographs without and with subtraction images using an independent test method. The reviewing time for each radiologist was recorded in each case. The observers' performance was evaluated by use of receiver operating characteristic (ROC) analysis. RESULTS: When subtraction images were available, the mean reviewing time per case was reduced significantly from 13.6 to 10.8 seconds for the cases with newly developed abnormalities (p < 0.001) and from 29.8 to 14.1 seconds for negative cases (p < 0.001). The reduction in the mean reviewing time with subtraction images was greater for clean images than for images with artifacts (17.7 vs 14.5 seconds, p < 0.001). The average mean area under the ROC curve value increased significantly from 0.942 without subtraction images to 0.988 with subtraction images (p = 0.025). There were significant differences in the sensitivity (0.963 with and 0.888 without the subtraction images) and the specificity (0.976 with and 0.899 without the subtraction images) (p < 0.001). CONCLUSION: The temporal subtraction technique can reduce reviewing time and also improve diagnostic accuracy in the interpretation of chest radiographs.

Etidronic Acid↗
PubMed2005

Intracranial aneurysms at MR angiography: effect of computer-aided diagnosis on radiologists' detection performance.

PURPOSE: To retrospectively evaluate the effect of computer-aided detection (CAD) on radiologists' performance in detection of intracranial aneurysms with magnetic resonance (MR) angiography. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Adult↗
PubMed2005

Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs.

RATIONALE AND OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Database Management Systems↗
PubMed2004

Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening.

RATIONALE AND OBJECTIVES: A computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening was developed. MATERIALS AND METHODS: Our scheme is based on a difference-image technique for enhancing the lung nodules and suppressing the majority of background normal structures. The difference image for each computed tomography image was obtained by subtracting the nodule-suppressed image processed with a ring average filter from the nodule-enhanced image with a matched filter. The initial nodule candidates were identified by applying a multiple-gray level thresholding technique to the difference image, where most nodules were well enhanced. A number of false-positives were removed first in entire lung regions and second in divided lung regions by use of the two rule-based schemes on the localized image features related to morphology and gray levels. Some of the remaining false-positives were eliminated by use of a multiple massive training artificial neural network trained for reduction of various types of false-positives. This computerized scheme was applied to a confirmed cancer database of 106 low-dose computed tomography scans with 109 cancer lesions for 73 patients obtained from a lung cancer screening program in Nagano, Japan. RESULTS: This computed-aided diagnosis scheme provided a sensitivity of 83% (91/109) for all cancers with 5.8 false-positives per scan, which included 84% (32/38) for missed cancers with 5.9 false-positives per scan. CONCLUSION: This computerized scheme may be useful for assisting radiologists in detecting lung cancers on low-dose computed tomography images for lung cancer screening.

False Positive Reactions↗
PubMed2004

Automated computerized scheme for detection of unruptured intracranial aneurysms in three-dimensional magnetic resonance angiography.

RATIONALE AND OBJECTIVES: 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). MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: This computer-aided diagnostic system would be useful in assisting radiologists for the detection of intracranial aneurysms in magnetic resonance angiography.

False Positive Reactions↗
PubMed2004

Effect of temporal subtraction images on radiologists' detection of lung cancer on CT: results of the observer performance study with use of film computed tomography images.

RATIONALE AND OBJECTIVES: To evaluate the effect of temporal subtraction images on the radiologists' detection of early primary lung cancer in computed tomography (CT) scans. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: The temporal subtraction technique can significantly improve the sensitivity and specificity for detection of lung cancer on CT scans.

Adult↗
PubMed2004

Potential usefulness of biological fingerprints in chest radiographs for automated patient recognition and identification.

RATIONALE AND OBJECTIVES: The purpose of this study was to demonstrate the potential usefulness of "biological fingerprints" in chest radiographs for automated patient recognition and identification. MATERIALS AND METHODS: Thoracic fields, cardiac shadows, the superior mediastinum, lung apices, a part of the right lung, and the right lower lung that includes the costophrenic angle were used as biological fingerprints in chest radiographs. Each of the biological fingerprints in a current chest radiograph was used as a template for determination of the correlation value with the corresponding biological fingerprint in a previous chest radiograph for patient recognition and identification. The overall performance of the method developed was examined in terms of receiver operating characteristic curves. RESULTS: Receiver operating characteristic curves obtained with different biological fingerprints, except for the part of the right lung, indicated a high performance in identifying patients. These results showed that a new concept of biological fingerprints in radiologic images would be useful in patient recognition and identification. The low performance with the part of the right lung seems to be related to a general observation that this region does not usually include features unique to a specific patient. The performance of the artificial neural networks by use of a combination of five biological fingerprints was higher than results obtained with each biological fingerprint. CONCLUSION: The use of automated patient identification based on biological fingerprints in chest radiographs is promising for helping to discover misfiled patient images, especially in a picture archiving and communication system environment.

Dermatoglyphics↗
PubMed2004

Improved detection of lung cancer arising in diffuse lung diseases on chest radiographs using temporal subtraction.

RATIONALE AND OBJECTIVES: The purpose of this study was to evaluate the usefulness of temporal subtraction for the detection of lung cancer arising in pneumoconiosis, idiopathic pulmonary fibrosis, and pulmonary emphysema. MATERIALS AND METHODS: Fifteen cases of lung cancer arising in diffuse lung diseases, including three cases of pneumoconiosis, six of idiopathic pulmonary fibrosis, and six of pulmonary emphysema, were evaluated. Pathologic proof was obtained by surgery or transbronchial lung biopsy. The average interval between previous and current radiographs was 356 days (range, 31-947 days). All chest radiographs were obtained with a computed radiography system, and temporal subtraction images were produced by subtracting of a previous image from a current one with a nonlinear image-warping technique. The effect of the temporal subtraction image was evaluated by observer performance study with receiver operating characteristic analysis. RESULTS: The average observer performance with temporal subtraction was significantly improved (Az = 0.935) compared with that without temporal subtraction (Az = 0.857, P < .0001). CONCLUSION: The temporal subtraction technique is useful for the detection of lung cancer arising in pneumoconiosis, idiopathic pulmonary fibrosis, and pulmonary emphysema.

Adenocarcinoma↗
PubMed2004

Computer-aided diagnosis scheme for histological classification of clustered microcalcifications on magnification mammograms.

The histological classification of clustered microcalcifications on mammograms can be difficult, and thus often require biopsy or follow-up. Our purpose in this study was to develop a computer-aided diagnosis scheme for identifying the histological classification of clustered microcalcifications on magnification mammograms in order to assist the radiologists' interpretation as a "second opinion." Our database consisted of 58 magnification mammograms, which included 35 malignant clustered microcalcifications (9 invasive carcinomas, 12 noninvasive carcinomas of the comedo type, and 14 noninvasive carcinomas of the noncomedo type) and 23 benign clustered microcalcifications (17 mastopathies and 6 fibroadenomas). The histological classifications of all clustered microcalcifications were proved by pathologic diagnosis. The clustered microcalcifications were first segmented by use of a novel filter bank and a thresholding technique. Five objective features on clustered microcalcifications were determined by taking into account subjective features that experienced the radiologists commonly use to identify possible histological classifications. The Bayes decision rule with five objective features was employed for distinguishing between five histological classifications. The classification accuracies for distinguishing between three malignant histological classifications were 77.8% (7/9) for invasive carcinoma, 75.0% (9/12) for noninvasive carcinoma of the comedo type, and 92.9% (13/14) for noninvasive carcinoma of the noncomedo type. The classification accuracies for distinguishing between two benign histological classifications were 94.1% (16/17) for mastopathy, and 100.0% (6/6) for fibroadenoma. This computerized method would be useful in assisting radiologists in their assessments of clustered microcalcifications.

Algorithms↗
PubMed2004

Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system.

OBJECTIVE: The aim of this study was to evaluate the usefulness of a new commercially available computer-aided diagnosis (CAD) system with an automated method of detecting nodules due to lung cancers on chest radiograph. MATERIALS AND METHODS: For patients with cancer, 45 cases with solitary lung nodules up to 25 mm in diameter (nodule size range, 8-25 mm in diameter; mean, 18 mm; median, 20 mm) were used. For healthy patients, 45 cases were selected on the basis of confirmation on chest CT. All chest radiographs were obtained with a computed radiography system. The CAD output images were produced with a newly developed CAD system, which consisted of an image server including CAD software called EpiSight/XR. Eight radiologists (four board-certified radiologists and four radiology residents) participated in observer performance studies and interpreted both the original radiographs and CAD output images using a sequential testing method. The observers' performance was evaluated with receiver operating characteristic analysis. RESULTS: The average area under the curve value increased significantly from 0.924 without to 0.986 with CAD output images. Individually, the use of CAD output images was more beneficial to radiology residents than to board-certified radiologists. CONCLUSION: This CAD system for digital chest radiographs can assist radiologists and has the potential to improve the detection of lung nodules due to lung cancer.

Adult↗
PubMed2004

Application of an artificial neural network to high-resolution CT: usefulness in differential diagnosis of diffuse lung disease.

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Adolescent↗