Fig 5 -

Input image (A) and target (B) alongside visualizations of the first 16 channels of intermediate activations for the given input image produced by early (1), middle (2) and late (3) convolutional layers in U-net (C) and MoNet (D). Histograms computed for all channels in the feature maps for early (1), middle (2), and late (3) convolution layers for U-net (E) and MoNet (F).</p

Axial slice of a ground truth pancreas segmentation in an abdominal CT scan (MSD), cropped to show detail of surrounding tissues.

Axial slice of a ground truth pancreas segmentation in an abdominal CT scan (MSD), cropped to show detail of surrounding tissues.</p

Comparison of <i>MoNet</i> with other <i>U-Net</i> variants in two different imaging modalities on the task of pancreas and brain lesion segmentation, CT and MRI respectively.

We report performance on validation sets of the MSD datasets (brain tumor and pancreas) as well as out-of sample generalization performance on an independent validation set (IVD), collected and annotated in-house.</p

CPU inference time (sec) for a CT scan of 150 slices and timer per batch (sec) on GPU, both at a resolution of 256 × 256.

CPU inference time (sec) for a CT scan of 150 slices and timer per batch (sec) on GPU, both at a resolution of 256 × 256.</p

Comparison of storage space occupied by <i>MoNet</i> and other <i>U-Net</i> variants.

Comparison of storage space occupied by MoNet and other U-Net variants.</p

Fig 4 -

Exemplary segmentation results (yellow) of: U-Net-16 (A), Attention U-Net (B), U-Net-64 (C), MoNet (D), on the pancreas MSD validation set, Ground truth indicated by red outline. Box-plots of Hausdorff distances (E) and Dice scores (F) computed for the whole pancreas MSD validation set on a per-patient basis.</p

Schematic representation diagram of a <i>RDDC</i> block (top) and the constituent convolutional (bottom).

Schematic representation diagram of a RDDC block (top) and the constituent convolutional (bottom).</p

Additional file 2: of Use of quantitative T2 mapping for the assessment of renal cell carcinomas: first results

Figure S2. Segmentation of tumors (ISUP grades 1 to 4 from top to bottom, A-D) with creation of image masks (A2 through D2). A3 through D3 show the calculated percentage densities of the absolute T2 values. (TIF 2244 kb

Schematic representation of the <i>MoNet</i> architecture.

Schematic representation of the MoNet architecture.</p

Additional file 3: of Use of quantitative T2 mapping for the assessment of renal cell carcinomas: first results

Figure S3. The upper part of the figure illustrates the percentage density of T2 values for whole-tumor measurements of the four colour-coded ISUP grades (refer to the legend on the upper right side). For each tumor, the T2 maps were segmented and image masks were imported into the open access software ‘R’. The lower part of the figure shows the colour-coded percentage density of T2 values for lower grade tumors (combined ISUP grades 1 and 2) and higher grade tumors (combined ISUP grades 3 and 4). (TIF 773 kb