Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography

Purpose: A massive-training artificial neural network MTANN has been developed for the reduction of false positives FPs in computer-aided detection CADe of polyps in CT colonography CTC. A major limitation of the MTANN is the long training time. To address this issue, the authors investigated the feasibility of two state-of-the-art regression models, namely, support vector regression SVR and Gaussian process regression GPR models, in the massive-training framework and developed massive-training SVR MTSVR and massive-training GPR MTGPR for the reduction of FPs in CADe of polyps. Methods: The authors applied SVR and GPR as volume-processing techniques in the distinction of polyps from FP detections in a CTC CADe scheme. Unlike artificial neural networks ANNs, both SVR and GPR are memory-based methods that store a part of or the entire training data for testing. Therefore, their training is generally fast and they are able to improve the efficiency of the massivetraining methodology. Rooted in a maximum margin property, SVR offers excellent generalization ability and robustness to outliers. On the other hand, GPR approaches nonlinear regression from a Bayesian perspective, which produces both the optimal estimated function and the covariance associated with the estimation. Therefore, both SVR and GPR, as the state-of-the-art nonlinear regression models, are able to offer a performance comparable or potentially superior to that of ANN, with highly efficient training. Both MTSVR and MTGPR were trained directly with voxel values from CTC images. A 3D scoring method based on a 3D Gaussian weighting function was applied to the outputs of MTSVR and MTGPR for distinction between polyps and nonpolyps. To test the performance of the proposed models, the authors compared them to the original MTANN in the distinction between actual polyps and various types of FPs in terms of training time reduction and FP reduction performance. The authors’ CTC database consisted of 240 CTC data sets obtained from 120 patients in the supine and prone positions. The training set consisted of 27 patients, 10 of which had 10 polyps. The authors selected 10 nonpolyps i.e., FP sources from the training set. These ten polyps and ten nonpolyps were used for training the proposed models. The testing set consisted of 93 patients, including 19 polyps in 7 patients and 86 negative patients with 474 FPs produced by an original CADe scheme. Results: With the MTSVR, the training time was reduced by a factor of 190, while a FP reduction performance by-polyp sensitivity of 94.7% 18/19 with 2.5 230/93 FPs/patient comparable to that of the original MTANN the same sensitivity with 2.6 244/93 FPs/patient was achieved. The classification performance in terms of the area under the receiver-operating-characteristic curve value of the MTGPR 0.82 was statistically significantly higher than that of the original MTANN 0.77, with a two-sided p-value of 0.03. The MTGPR yielded a 94.7% 18/19 by-polyp sensitivity at a FP rate of 2.5 235/93 per patient and reduced the training time by a factor of 1.3. Conclusions: Both MTSVR and MTGPR improve the efficiency of the training in the massivetraining framework while maintaining a comparable performance. © 2011 American Association of Physicists in Medicine. DOI: 10.1118/1.3562898