Hepatocellular Carcinoma (HCC) is a worldwide tumor, but the prognosis can be improved by early diagnosis. In contrast-enhanced CT, a modality commonly used for HCC diagnosis, HCC lesion represents dynamic enhancement patterns. To incorporate HCC dynamic characteristic in multi-phase into an automatic lesion detection system, multiphase CT images were aligned by using image registration scheme. The registered artery, portal venous and delayed phase images were merged into one RGB image. 2D based deep convolutional neural network (DCNN) detection model was trained and tested in total of 251 CT dataset. The performance of the proposed DCNN model with dynamic multiphase information showed a sensitivity of 93.88% in the false positives (FPs) of 2.98/patient in 52 test CT dataset. This result is better than the best performance among three single phase settings with sensitivity of 73.47% at 3.15 FPs/patient, indicating that the inclusion of dynamic information in multi-phase CT images is more effective in HCC detection.
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