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.
Effective segmentation of abdominal organs on CT images is necessary not only in the quantitative analysis but also in the dose simulation of radiational oncology. However, the manual or semi-automatic segmentation is tedious and subject to inter- and intra-observer variances. To overcome these shortcomings, the development of a fully automatic segmentation is required. In this paper, we propose the deep learning based fully-automated method to segment multiple organs from abdominal CT images and evaluate its performance on clinical dataset. Total 120 cases were used for training and testing. The DSC values in 20 test dataset were 0.945±0.016, 0.836±0.084, 0.912±0.052 and 0.886±0.068 for the liver, stomach, right and left kidney, respectively.