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Fig. 1 | Cancer Imaging

Fig. 1

From: A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images

Fig. 1

The HFS-Net data flow for liver and tumor segmentation. Stage I: Identify tumor’s longest axis in every slice of a case. Stage II: Accord tumor size by feeding the CT slice which the longest axis of tumors over 30 pixels in the slice for flarge computation and which the longest axis less than 30 pixels of tumors in the slice for fsmall computation. Stage III: Combine venous phases of CT images and results of fliver, fsize, flarge, and fsmall as input of f3D computation for getting final segmentation of tumors

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Cancer Imaging

ISSN: 1470-7330

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