Hiro Yoshida

CAD techniques, challenges, and controversies in computed tomographic colonography.

Computer-aided diagnosis (CAD) for computed tomographic colonography (CTC) automatically detects the locations of suspicious polyps and masses on CTC and provides radiologists with a second opinion. CAD has the potential to increase radiologists’ diagnostic performance in the detection of polyps and masses and to decrease variability of the diagnostic accuracy among readers without significantly increasing the reading time. Technical developments have advanced CAD substantially during the past several years, and a fundamental scheme for the detection of polyps has been established. The most recent CAD systems based on this scheme produce a clinically acceptable high sensitivity and a low false-positive rate. However, CAD for CTC is still under active development, and the technology needs to be improved further. This report describes the expected benefits, the current fundamental scheme, the key techniques used for detection of polyps and masses on CTC, the current detection performance, as well as the pitfalls, challenges, controversies, and the future of CAD.

Virtual colonoscopy: past, present, and future

Virtual colonoscopy is developing into a practical clinical technique. The issues of the steep learning curve and accuracy of the technique are undergoing advances related to patient preparation, scanning technique, reading methods, and CAD. It is probably the best test for patients with an incomplete colonoscopy or for those patients who cannot undergo colonoscopy. Its precise role in screening average-risk patients for colon cancer remains to be defined by ongoing research and clinical trials.

Introduction to Computational Intelligence in Healthcare

This chapter presents introductory remarks on computational intelligence in healthcare practice, and it provides a brief outline for each of the succeeding chapters in the remainder of this book.

Fecal-tagging CT colonography with structure-analysis electronic cleansing for detection of colorectal flat lesions

PURPOSE To evaluate the feasibility and sensitivity of the 3D-reading of fecal-tagging CT colonography (CTC) with a novel structure-analysis electronic cleansing (SAEC) in detecting colorectal flat lesions in comparison with a cleansed 3D reading with Viatronix V3D Colon system (V3D) and primary uncleansed 2D reading (2D). MATERIALS AND METHODS Forty CTC cases with flat lesions were retrospectively observed. The Subjects from a multicenter clinical trial underwent cathartic bowel preparation with orally administrated barium-based fecal-tagging. Sixty-nine flat lesions were confirmed using colonoscopy and histopathology as a reference standard. The results from SAEC reading were compared with those of prospective V3D and 2D readings. RESULTS Overall detection sensitivity with SAEC was 52% (36/69), which was statistically higher than that of 32% (22/69) and 29% (20/69) with V3D and 2D readings, respectively (p<0.05). The sensitivities in detecting not-on-fold flat lesions were 63% (24/38), 45% (17/38), and 42% (16/38) with SAEC, V3D, and 2D readings, respectively; whereas those of on-fold flat lesions were 39% (12/31), 16% (5/31), and 13% (4/31), respectively. None of the eight flat lesions (2-9mm) at cecum was detected by any of the three reading methods. Excluding the flat lesions at cecum, the sensitivity with SAEC for detecting flat lesion ≥4mm increased to 84% (31/37). CONCLUSIONS The fecal-tagging CTC with structure-analysis electronic cleansing could yield a high sensitivity for detecting flat lesions ≥4mm. The not-on-fold flat lesions were detected with higher sensitivity than on-fold flat lesions.

TU‐EE‐A3‐04: Massive Training Artificial Neural Network (MTANN) to Reduce False Positives Due to Rectal Tubes in Computer‐Aided Polyp Detection

Purpose: One limitation of current computer‐aided detection (CAD) of polyps in CT colonography is a relatively large number of false positives. Rectal tubes are a common source of false positives and may distract the reader from less common polyps in the rectum. Our purpose was to develop a three‐dimensional massive‐training artificial neural network (3D MTANN) for reduction of false positives due to rectal tubes generated by a CAD scheme. Material and Methods: Our database consisted of CT colonography of 73 patients, scanned in both supine and prone positions. Fifteen patients had 28 polyps (15 polyps: 5–9 mm; 13 polyps: 10–25 mm). These cases were subjected to our previously reported CAD scheme that included shape‐based detection of polyps and reduction of false positives with a Bayesian neural network. With this scheme, 96.4% (27/28) by‐polyp sensitivity with 3.1 (224/73) false positives per patient was achieved. To eliminate false‐positive rectal tubes, we developed a 3D MTANN that was trained to enhance polyps and suppress rectal tubes. Results: In the output volumes of the trained 3D MTANN, various polyps were represented by distributions of bright voxels, whereas rectal tubes appeared as darker voxels. The 3D MTANN removed all 20 false‐positive rectal tubes produced by our original CAD scheme without removing any true positives. To evaluate the overall performance, we applied the 3D MTANN to the entire database containing 27 polyps (true positives) and 224 non‐polyps (false positives). The 3D MTANN eliminated 33% (73/224) of non‐polyps without removal of any true positives in an independent test. Conclusion: The 3D MTANN was able to improve the false‐positive rate of our original CAD scheme from 3.1 to 2.1 false positives per patient, while an original by‐polyp sensitivity of 96.4% was maintained. Conflict of Interest: HY, SGA: shareholders, R2 Technology, Inc.