An MTANN CAD for detection of polyps in false-negative CT colonography cases in a large multicenter clinical trial: preliminary results

A major challenge in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the detection of "difficult" polyps which radiologists are likely to miss. Our purpose was to develop a CAD scheme incorporating massive-training artificial neural networks (MTANNs) and to evaluate its performance on false-negative (FN) cases in a large multicenter clinical trial. We developed an initial polyp-detection scheme consisting of colon segmentation based on CT value-based analysis, detection of polyp candidates based on morphologic analysis, and quadratic discriminant analysis based on 3D pattern features for classification. For reduction of false-positive (FP) detections, we developed multiple expert 3D MTANNs designed to differentiate between polyps and seven types of non-polyps. Our independent database was obtained from CTC scans of 155 patients with polyps from a multicenter trial in which 15 medical institutions participated nationwide. Among them, about 45% patients received FN interpretations in CTC. For testing our CAD, 14 cases with 14 polyps/masses were randomly selected from the FN cases. Lesion sizes ranged from 6-35 mm, with an average of 10 mm. The initial CAD scheme detected 71.4% (10/14) of "missed" polyps, including sessile polyps and polyps on folds, with 18.9 (264/14) FPs per case. The 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 case. With our CAD scheme incorporating MTANNs, 71.4% of polyps "missed" by radiologists in the trial were detected correctly, with a reasonable number of FPs.

[1]  C. Mulrow,et al.  Colorectal cancer screening: clinical guidelines and rationale. , 1997, Gastroenterology.

[2]  Kenji Suzuki,et al.  Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Jianhua Yao,et al.  CT colonography with computer-aided detection: automated recognition of ileocecal valve to reduce number of false-positive detections. , 2004, Radiology.

[4]  Y. Masutani,et al.  Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. , 2002, Radiology.

[5]  Hiroyuki Yoshida,et al.  Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps , 2001, IEEE Transactions on Medical Imaging.

[6]  Abraham H. Dachman,et al.  Atlas of Virtual Colonoscopy , 2003, Springer New York.

[7]  Michael Macari,et al.  CT colonography: where have we been and where are we going? , 2005, Radiology.

[8]  Marek Franaszek,et al.  Support vector machines committee classification method for computer-aided polyp detection in CT colonography. , 2005, Academic radiology.

[9]  Kenji Suzuki,et al.  Efficient approximation of neural filters for removing quantum noise from images , 2002, IEEE Trans. Signal Process..

[10]  S. Armato,et al.  Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. , 2003, Medical physics.

[11]  K. Doi,et al.  False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. , 2005, Academic radiology.

[12]  Kunio Doi,et al.  Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network , 2005, IEEE Transactions on Medical Imaging.

[13]  Ronald M Summers,et al.  Reduction of false positives on the rectal tube in computer-aided detection for CT colonography. , 2004, Medical physics.

[14]  Carlo Tomasi,et al.  A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography , 2001, IEEE Transactions on Medical Imaging.

[15]  Kunio Doi,et al.  Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN) , 2006, IEEE Transactions on Medical Imaging.

[16]  H. Yoshida,et al.  Automated detection of polyps with CT colonography: evaluation of volumetric features for reduction of false-positive findings. , 2002, Academic radiology.

[17]  Carlo Tomasi,et al.  Edge displacement field-based classification for improved detection of polyps in CT colonography , 2002, IEEE Transactions on Medical Imaging.

[18]  H. Yoshida,et al.  CAD techniques, challenges, andcontroversies in computed tomographic colonography , 2004, Abdominal Imaging.

[19]  Hiroyuki Yoshida,et al.  Computer-aided diagnosis scheme for detection of polyps at CT colonography. , 2002, Radiographics : a review publication of the Radiological Society of North America, Inc.

[20]  J G Fletcher,et al.  CT colonography: unraveling the twists and turns , 2005, Current opinion in gastroenterology.

[21]  Ronald M. Summers,et al.  Support vector machines committee classification method for computer-aided polyp detection in CT colonography1 , 2005 .

[22]  Joyoni Dey,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[23]  Kenji Suzuki,et al.  CT colonography: false-negative interpretations. , 2007, Radiology.

[24]  Kunio Doi,et al.  Effect of a small number of training cases on the performance of massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose CT , 2003, SPIE Medical Imaging.

[25]  I. Bitter,et al.  Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. , 2005, Gastroenterology.

[26]  Zhengrong Liang,et al.  Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy. , 2005, Medical physics.

[27]  Marek Franaszek,et al.  Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets. , 2003, Academic radiology.

[28]  Hiroyuki Yoshida,et al.  Region-based supine-prone correspondence for the reduction of false-positive CAD polyp candidates in CT colonography. , 2005, Academic radiology.

[29]  Kenji Suzuki,et al.  Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector , 2004, IEEE Transactions on Medical Imaging.

[30]  Hiroyuki Yoshida,et al.  Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes. , 2006, Medical physics.

[31]  E. Paulson,et al.  Analysis of air contrast barium enema, computed tomographic colonography, and colonoscopy: prospective comparison , 2005, The Lancet.

[32]  A. M. Youssef,et al.  Automated polyp detection at CT colonography: feasibility assessment in a human population. , 2001, Radiology.

[33]  J. Malley,et al.  Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees. , 2002, Medical physics.

[34]  A. Jemal,et al.  Cancer Statistics, 2005 , 2005, CA: a cancer journal for clinicians.

[35]  Guy Marchal,et al.  Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods , 2002, European Radiology.

[36]  Hiroyuki Yoshida,et al.  Computer-aided diagnosis for CT colonography. , 2004, Seminars in ultrasound, CT, and MR.

[37]  Kunio Doi,et al.  How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? , 2005, Academic radiology.