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Fig. 3 | Cancer Imaging

Fig. 3

From: Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos

Fig. 3

A comparison of image segmentation algorithms in the validation cohorts. Ground truth and predicted mask of tumors are labeled in yellow and cyan-blue, respectively. Compared with other algorithms, the FFS model achieved the lowest false positive and missed segmentation in the following four situations: multiple lesions (patient 1), a small lesion < 3 cm (patient 2), a small lesion < 3 cm with obvious surrounding stomach and intestine images (patient 3), and poor image quality (patient 4). TDL, temporal difference learning; LRS, liver region segmentation; FFS, final fusion segmentation

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Cancer Imaging

ISSN: 1470-7330

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