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

Figure 9

An example showing how deep fusion network- (DFN-) R helps improve segmentation accuracy. (a) Ground tumor (GT) and segmentation results by (b) DFN-F and (c) DFN-R on hepatobiliary phase- (HBP-) magnetic resonance imaging (MRI) are presented in indigo, green, and light purple, respectively. The intervention by the radiologist (green rectangular) differentiated the hepatocellular carcinoma (HCC) lesion from its surrounding tissue with similar intensity which may be misclassified as tumor lesions by DFN-F. Therefore, by using DFN-R, the target area is strictly restricted, and the lesion can be correctly distinguished and segmented.

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