Deep Learning Combined with Radiologist’s Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images

Figure 7

Two examples of the segmentation results by deep fusion network- (DFN-) F. (a, e) Ground truth (GT) (first column), (b, f) segmentation results by DFN-F (second column), (c, g) subdeep convolutional neural networks (DCNNs) (in hepatobiliary phase- (HBP-) magnetic resonance imaging (MRI), third column), and (d, h) sub-DCNN (portal venous phase- (PVP-) MRI, fourth column) are presented in indigo, green, blue, and light red, respectively. In the (a–d) first example, two (c, d) sub-DCNNs successfully recognized only part of the hepatocellular (HCC), while DFN-F successfully segmented the HCC lesions correctly. In the (e–h) second example, the sub-DCNN in (g) HBP-MRI misclassified normal tissues as tumor lesions, while DFN-F avoided false segmentation by taking the information of both (h) PVP-MRI and (g) HBP-MRI into consideration.

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