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European Radiology Experimental
Fig. 2 | European Radiology Experimental

Fig. 2

From: Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

Fig. 2

Scheme of the two-step fully automated affine registration algorithm using intensity masks. First, an independently developed two-dimensional liver segmentation algorithm was used to extract liver masks populated with intensities. Intensity masks were registered using an affine transformation network to geometrically align the moving series (follow-up) to the static series (baseline). Optimal affine transformation parameters were determined by maximizing the similarity between baseline and registered follow-up. Affine transformation parameters were then used to map the entire moving series to the static image series space

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