The goal of the proposed research is to develop a 12three-dimensional massive-training artificial neural network (3D MTANN) for a computer-aided diagnostic (CAD) scheme for detection of colorectal polyps in computed tomographic colonography (CTC). The CAD output will be used as a """"""""second opinion"""""""" to assist radiologists in detecting polyps for early detection of colorectal cancer. We will develop a CAD scheme incorporating a 3D MTANN for distinction between polyps and non-polyps (false positives) to reduce the number of false positives as much as possible, while maintaining a high sensitivity level. The 3D MTANN is a 3D volume-processing technique based on an artificial neural network which is capable of operating on image data directly. With input CTC volumes and the corresponding teaching volumes, the 3D MTANN can be trained for enhancement of polyps and suppression of non-polyps. We plan to develop a multiple 3D MTANN scheme (multi-3D MTANN) consisting of several expert 3D MTANNs for reduction of various types of false positives including folds, stool, the ileocecal valve, and rectal tubes. By applying a scoring method on the output volumes of the 3D MTANNs, polyp candidates will be classified as polyps or non-polyps. We will compare 3D MTANNs with two-dimensional MTANNs in terms of performance, efficiency, and properties. To obtain reliable evaluation results, we will collect a large database of CTC cases with and without polyps. By comparing with the diagnostic report of the gold standard optical colonoscopy on the same patients, we will determine """"""""missed"""""""" cases which are false-negative cases when radiologists read CTC images. We will develop a prototype CAD workstation based on an advanced CAD system incorporating the multi-3D MTANN, and evaluate the performance of the workstation with the database by free-response receiver operating characteristic (FROC) analysis. We plan to carry out an observer performance study to evaluate the potential usefulness of the CAD scheme by use of multi-reader multi-case receiver operating characteristic analysis. The CAD system incorporating with the multi-3D MTANN will provide radiologists with the location of highly suspected lesions, and it has the potential to improve diagnostic accuracy in the early detection of colorectal cancer, which may lead to improved prognosis of patients. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA120549-02
Application #
7500326
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Croft, Barbara
Project Start
2007-09-26
Project End
2011-07-31
Budget Start
2008-08-01
Budget End
2009-07-31
Support Year
2
Fiscal Year
2008
Total Cost
$291,650
Indirect Cost
Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Epstein, Mark L; Obara, Piotr R; Chen, Yisong et al. (2015) Quantitative radiology: automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing. Quant Imaging Med Surg 5:673-84
Xu, Jian-Wu; Suzuki, Kenji (2014) Max-AUC feature selection in computer-aided detection of polyps in CT colonography. IEEE J Biomed Health Inform 18:585-93
Suzuki, Kenji (2013) Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey. IEICE Trans Inf Syst E96-D:772-783
Suzuki, Kenji (2012) Pixel-based machine learning in medical imaging. Int J Biomed Imaging 2012:792079
Xu, Jian-Wu; Suzuki, Kenji (2011) Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. Med Phys 38:1888-902
He, Lifeng; Chao, Yuyan; Suzuki, Kenji (2011) Two efficient label-equivalence-based connected-component labeling algorithms for 3-D binary images. IEEE Trans Image Process 20:2122-34
Chen, Sheng; Suzuki, Kenji; MacMahon, Heber (2011) Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification. Med Phys 38:1844-58
Suzuki, Kenji; Rockey, Don C; Dachman, Abraham H (2010) CT colonography: advanced computer-aided detection scheme utilizing MTANNs for detection of ""missed"" polyps in a multicenter clinical trial. Med Phys 37:12-21
Suzuki, Kenji; Zhang, Jun; Xu, Jianwu (2010) Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Trans Med Imaging 29:1907-17
Shi, Zhenghao; He, Lifeng; Suzuki, Kenji et al. (2009) Survey on Neural Networks Used for Medical Image Processing. Int J Comput Sci 3:86-100

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