Fig. 2.

The DL architecture of the proposed 3D segmentation framework (three segmentation CNNs + one ensembler network) is shown. Each CNN (A) comprised of four micro-U-Nets (μ-U-Nets; B) and a latent space (LS; C). The three CNNs differed from each other only in the design of the ‘feature extraction’ (FE) units (D; Types 1-3). The ensembler (E) consisted of three sets of 3D convolutional layers, with each set separated by a dropout layer. ONH-Net (F) was then assembled by using the three trained CNNs as parallel input pipelines to the ensembler network.