Computer-aided Diagnosis Workstation Research Articles

A general framework for wireless capsule endoscopy study synopsis

We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The development of a computer-aided diagnosis (CAD) CE workstation will have a great potential to reduce the diagnostic time and improve the accuracy of assessment. We propose a general framework based on hidden Markov models (HMMs) for study synopsis that forms the computational engine of our CAD workstation. Color, edge and texture features are first extracted and analyzed by a Support Vector Machine classifier, and then encoded as the observations for the HMM, uniquely combining the temporal information during the assessment. Experiments were performed on 13 full-length CE studies, instead of selected images previously reported. The results (e.g. 0.933 accuracy with 0.933 recall for detection of polyps) show that our framework achieved promising performance for multiple classification. We also report the patient-level CAD assessment of complete CE studies for multiple abnormalities, and the patient-level validation demonstrates the effectiveness and robustness of our methods.

Breast US Computer-aided Diagnosis Workstation: Performance with a Large Clinical Diagnostic Population

To evaluate the performance of a computer-aided diagnosis (CAD) workstation in classifying cancer in a realistic data set representative of a clinical diagnostic breast ultrasonography (US) practice. The database consisted of consecutive diagnostic breast US scans collected with informed consent with a protocol approved by the institutional review board and compliant with the HIPAA. Images from 508 patients with a total of 1046 distinct abnormalities were used. One hundred one patients had breast cancer. Results both for patients in whom the lesion abnormality was proved with either biopsy or aspiration (n = 183) and for all patients irrespective of biopsy status (n = 508) are presented. The ability of the CAD workstation to help differentiate malignancies from benign lesions was evaluated with a leave-one-out-by-case analysis. The clinical specificity of the radiologists for this dataset was determined according to the biopsy rate and outcome. In the task of differentiating cancer from all other lesions sent to biopsy, the CAD workstation obtained an area under the receiver operating characteristic curve (AUC) value of 0.88, with 100% sensitivity at 26% specificity (157 cancers and 362 lesions total). The radiologists' specificity at 100% sensitivity for this set was zero. When analyzing all lesions irrespective of biopsy status, which is more representative of actual clinical practice, the CAD scheme obtained an AUC of 0.90 and 100% sensitivity at 30% specificity (157 cancers and 1046 lesions total). The radiologists' specificity at 100% sensitivity for this set was 77%. Current levels of computer performance warrant a clinical evaluation of the potential of US CAD to aid radiologists in lesion work-up recommendations.

Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system.

The aim of this study was to evaluate a computer-aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Eighty-eight consecutive spiral-CT examinations were reported by two radiologists in consensus. All examinations were reviewed using a CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm is designed to detect nodules with diameters of at least 5 mm. A total of 153 nodules were detected with at least one modality (radiologists in consensus, CAD, 85 nodules with diameter < 5 mm, 68 with diameter > or = 5 mm). The results of automatic nodule detection were compared to nodules detected with any modality as gold standard. Computer-aided diagnosis correctly identified 26 of 59 (38%) nodules with diameters > or = 5 mm detected by visual assessment by the radiologists; of these, CAD detected 44% (24 of 54) nodules without pleural contact. In addition, 12 nodules > or = 5 mm were detected which were not mentioned in the radiologist's report but represented real nodules. Sensitivity for detection of nodules > or = 5 mm was 85% (58 of 68) for radiologists and 38% (26 of 68) for CAD. There were 5.8+/-3.6 false-positive results of CAD per CT study. Computer-aided diagnosis improves detection of pulmonary nodules at spiral CT and is a valuable second opinion in a clinical setting for lung cancer screening despite of its still limited sensitivity.

Computer-aided diagnosis in radiology: potential and pitfalls

Computer-aided diagnosis (CAD) may be defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to improve the diagnostic accuracy and the consistency of the radiologists’ image interpretation. This article is to provide a brief overview of some of CAD schemes for detection and differential diagnosis of pulmonary nodules and interstitial opacities in chest radiographs as well as clustered micro-calcifications and masses in mammograms. ROC analysis clearly indicated that the radiologists’ performances were significantly improved when the computer output was available. An intelligent CAD workstation was developed for detection of breast lesions in mammograms. Results obtained from the first 10 000 cases indicated the potential of CAD in detecting approximately one-half of ‘missed’ breast cancer.