Machine/Data Learning

– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial

– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial

– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Sources in CAD for Polyp Detection in CT Colonography

– Eliminating Multiple False-Positive Sources in CAD for Polyp Detection in CT Colonography by Means of a Mixture of Expert 3D Massive-Training Artificial Neural Networks

– Reduction of False Positives in Computer-Aided Detection of Polyps in CT Colonography Using a Massive-Training Artificial Neural Network (MTANN): Suppression of Rectal Tubes

– Reduction of Quantum Noise in Low-Dose Double-Contrast Radiographs of the Stomach

– Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes

– Computerized Detection of Lung Nodules in Low-Dose CT, Part I: Basic Principle of Massive-Training Artificial Neural Network (MTANN) for Reduction of False Positives

– Computerized Detection of Lung Nodules in Low-Dose CT, Part II: Usefulness of Multiple Massive-Training Artificial Neural Networks (Multi-MTANNs)

– Massive-Training Artificial Neural Network (MTANN) Trained with a Small Number of Cases for Enhancement of Nodules and Suppression of Vessels in Thoracic CT: Phantom Experiments

– Reduction of False Positives in a CAD Scheme for Detection of Lung Nodules on MDCT by Use of 3D Massive-Training Artificial Neural Network

– Computer-aided Diagnostic Scheme for Distinction between Benign and Malignant Nodules in Thoracic Low-Dose CT by Use of a Massive-Training Artificial Neural Network

– False-Positive Reduction in Computer-Aided Diagnostic Scheme for Detection of Nodules on Chest Radiographs by Means of Massive-Training Artificial Neural Network (MTANN)

– Virtual Dual-Energy Radiography: Image-Processing Technique for Suppressing Ribs in Chest Radiographs by Means of Massive-Training Artificial Neural Network (MTANN)

– Improving the Conspicuity of Nodules in Chest Radiographs by Use of Virtual Dual-Energy Radiography

– Computer-Aided Diagnostic System for Detection and Estimation of Coronary Artery Stenosis by Use of a Linear-Output Artificial Neural Network

– Reduction of Quantum Noise and Radiation Dose in Coronary Angiography by Means of a Neural Filter

– Extraction of Left Ventricular Contours from Left Ventriculograms by Means of a Neural Edge Detector

– Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images

– Analysis of the Neural Edge Enhancer Trained for Edge Enhancement in Noisy Images

– Reduction of Noise from Images by Use of a Neural Filter

– A Method for Designing the Optimal Structure of a Neural Filter

– Efficient Approximation of Neural Filters for Removing Quantum Noise from Images

– Determining the Receptive Field of a Neural Filter

A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial

We developed a computer-aided diagnostic (CAD) scheme for detection of flat lesions (also called flat polyps or depressed polyps) in CT colonography (CTC) in a large multicenter clinical trial in collaboration with Don C. Rockey, M.D., at the Southwest Medical Center of the University of Texas. Flat lesions in the colon are a major source of false-negative interpretations in CTC. A major challenge in CAD schemes is the detection of flat lesions, because existing CAD schemes are designed for detecting the common bulbous polyp shape. Our purpose was to develop a CAD scheme for detection of flat lesions in CTC. We developed a CAD scheme consisting of colon segmentation based on histogram and morphologic analysis, detection of polyp candidates based on intensity-based and morphologic feature analysis, and linear discriminant analysis for classification of the candidates as polyps or non-polyps. To detect flat lesions, we developed a tolerantEmorphologic analysis method in the polyp detection step for accommodating the analysis to include a flat shape. For reduction of false-positive (FP) detections, we developed 3D massive-training artificial neural networks (MTANNs) designed to differentiate flat lesions from various types of non-polyps. Our independent database consisted of CTC scans of 25 patients obtained from a multicenter clinical trial in which 15 institutions participated nationwide. Each patient was scanned in the supine and prone positions with collimations of 1.0-2.5 mm and reconstruction intervals of 1.0-2.5 mm. All patients underwent reference-standardEoptical colonoscopy. Flat lesions were determined under either heightE(< 3 mm high) or ratioE(height < 1/2 long axis) criteria. Twenty-eight flat lesions were identified. Among them, 11 (39%) were false negatives in CTC. Lesion sizes ranged from 6-18 mm, with an average of 9 mm. Our MTANN CAD scheme detected 68% (19/28) of flat lesions, including six lesions missedEby reporting radiologists in the original clinical trial, with 10 (249/25) FPs per patient. The figure shows examples of flat lesions, which are very small or on a fold (these are major causes of human misses). Some flat lesions are known to be histologically aggressive; therefore, detection of such lesions is critical clinically, but they are difficult to detect because of their uncommon morphology. It should be noted that these two cases were missedEby reporting radiologists in the original trial; thus, detection of these lesions may be considered very difficult.EOur scheme would be useful for detecting flat lesions which are a major source of false negatives, thus potentially improving radiologistsE sensitivity in their detection of polyps in CTC.

KenjiSuzuki_Newsletter_CTCCADFlatLesions_FigureS

Illustrations of flat lesions which exhibit uncommon flat morphology. (a) A flat lesion on a fold (10 mm; adenoma) in the cecum was detected correctly by our MTANN CAD scheme (indicated by an arrow). (b) A small flat lesion (6 mm; adenoma) in the cecum was detected correctly by our MTANN CAD scheme.

Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial

We developed computer-aided diagnostic (CAD) scheme for detection of polyps in CT colonography (CTC) and evaluating our CAD scheme with false-negative polyps in a large multicenter clinical trial in collaboration with Don C. Rockey, M.D., the Southwest Medical Center at the University of Texas. A major challenge in CAD schemes for detection of polyps in CTC is the detection of difficultEpolyps which radiologists are likely to miss. Our purpose was to develop a CAD scheme incorporating 3D massive-training artificial neural networks (3D MTANNs) and to evaluate its performance on false-negative (missedE cases in a large multicenter clinical trial. We developed an initial CAD scheme consisting of colon segmentation based on mathematical morphology, detection of polyp candidates based on intensity-based and morphologic feature analysis, and linear discriminant analysis for classification. For reduction of false-positive (FP) detections, we developed a mixtureEof seven expert 3D MTANNs designed to differentiate between polyps and seven types of non-polyps, including folds, stool, the ileocecal valve, and rectal tubes. Our independent database consisted of CTC scans of 614 patients obtained from a large multicenter clinical trial in which 15 institutions participated nationwide. Each patient was scanned in the supine and prone positions with collimations of 1.0-2.5 mm and reconstruction intervals of 1.0-2.5 mm. All patients underwent reference-standardEoptical colonoscopy. One hundred fifty-five patients had clinically significant polyps. Among them, about 45% patients received false-negative interpretations in CTC. For testing our CAD scheme with 3D MTANNs, 14 cases with 14 polyps/masses were randomly selected from the false-negative cases where lesions were visible in both supine and prone scans retrospectively. Lesion sizes ranged from 6-35 mm, with an average of 10 mm. The initial CAD scheme detected 71.4% (12/14) of missedEpolyps, including sessile polyps and polyps on folds, with 18.9 (264/14) FPs per patient. The 3D MTANNs removed 75% (197/264) of the FPs without loss of any true positives; thus, the performance of our CAD scheme was improved to 4.8 (67/14) FPs per patient. With our CAD scheme incorporating 3D MTANNs, 71.4% of polyps missedEby radiologists in the trial were detected correctly, with a reasonable number of FPs. Our CAD scheme would be useful for detecting difficultEpolyps which radiologists are likely to miss, thus potentially improving radiologistsEsensitivity in their detection of polyps in CTC.

Kenji Suzuki Newsletter CAD FN polypsS

Illustrations of polyps missedEby physicians in a multicenter clinical trial. Left: A very small polyp (6 mm) was detected correctly by our CAD system (indicated by an arrow). This polyp was not detected in either CTC or the gold standardEoptical colonoscopy in the trial. Right: A sessile-type polyp (a major source of human false negatives) was detected correctly by our CAD system.

Kenji Suzuki Newsletter CTC CAD FNCases

A patient with a small (7 mm) sessile polyp (one of the major sources of false-negative interpretations by radiologists) that was missedEin a clinical trial. (a) our CAD scheme incorporating 3D MTANNs correctly detected the polyp and pointed it by an arrow in an axial CTC image, (b) the polyp in the 3D endoluminal view, and (c) 3D volume rendering of the colon with three computer outputs indicated by yellow circles (one in the rectum is a true-positive detection and the other two are FP detections).