## 4 Results  
The DSC and JSC for each model are summarized in Table 2. Inference times are summarized in Table 3. The best agreement between model segmentation and manual segmentation was found for the ResNet34 model configuration with self-attention, the bottlenecked connection and the blurring mechanism. It achieved a DSC of 0.9359, JSC of 0.8874 and inference times of 33.56 ms/image and 42.3 s for all 1260 images of one DCE sequence. The worst agreement between model segmentation and manual segmentation was found for the ResNet50 with self-attention model configuration, with a DSC of 0.9210, JSC of 0.8670 and inference times of 194.8 ms/image and 245.2 s for all 1260 images of one DCE sequence. Upon visual inspection of the model segmentation results, it was found in all models that the lower edge of the segmented area was not straight, which influenced the results of the similarity coefficients. This phenomenon was an artefact caused by the data augmentation strategy, which allowed for reduced overfitting and better training outcomes. The training data were rarely not augmented before being inputted into the models, resulting in the horizontal line at the bottom of the manually segmented mask being interpreted as always inclined. A simple adjustment step was then taken to ensure the real-world usability metrics of the methodology. The refinement algorithm consists of a simple loop over the columns of the output of the model, forcing any pixel between the highest true value in a column and the 131 st from the bottom to be true. In other words, the generatedmasks are refined to only allow for pixels above the height of 131 from the bottom of the image to be true. The updated schematic of the solution can be seen below in Figure 6.  
The sole purpose of the refinement algorithm is to remove the bias inserted into the training process by the aggressive data augmentation strategy. Such an algorithm \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Configuration** & **Mean DSC \(\pm\) SD** & **Mean JSC \(\pm\) SD** \\ \hline Resnet18 & 0.9253 \(\pm\) 0.1034 & 0.8760 \(\pm\) 0.0758 \\ ResNet18 + SA & 0.9296 \(\pm\) 0.1028 & 0.8757 \(\pm\) 0.0765 \\ ResNet18 + BL & 0.9273 \(\pm\) 0.1033 & 0.8795 \(\pm\) 0.0764 \\ ResNet18 + BC & 0.9283 \(\pm\) 0.1045 & 0.8742 \(\pm\) 0.0763 \\ ResNet18 + SA + BL & 0.9348 \(\pm\) 0.1045 & 0.8846 \(\pm\) 0.0760 \\ ResNet18 + SA +BL + BC & 0.9293 \(\pm\) 0.1036 & 0.8755 \(\pm\) 0.0765 \\ Resnet34 & 0.9244 \(\pm\) 0.1017 & 0.8721 \(\pm\) 0.0756 \\ ResNet34 + SA & 0.9230 \(\pm\) 0.1012 & 0.8652 \(\pm\) 0.0752 \\ ResNet34 + BL & 0.9227 \(\pm\) 0.1015 & 0.8714 \(\pm\) 0.0749 \\ ResNet34 + BC & 0.9292 \(\pm\) 0.1009 & 0.8754 \(\pm\) 0.0755 \\ ResNet34 + SA + BL & 0.9337 \(\pm\) 0.1008 & 0.8780 \(\pm\) 0.0750 \\
**ResNet34 + SA + BL + BC** & **0.9359 \(\pm\) 0.1004** & **0.8874 \(\pm\) 0.0748** \\ Resnet50 & 0.9240 \(\pm\) 0.1055 & 0.8717 \(\pm\) 0.0781 \\ ResNet50 + SA & 0.9210 \(\pm\) 0.1069 & 0.8670 \(\pm\) 0.0790 \\ ResNet50 + BL & 0.9233 \(\pm\) 0.1063 & 0.8708 \(\pm\) 0.0777 \\ ResNet50 + BC & 0.9257 \(\pm\) 0.1059 & 0.8730 \(\pm\) 0.0775 \\ ResNet50 + SA + BL & 0.9278 \(\pm\) 0.1061 & 0.8727 \(\pm\) 0.0766 \\ ResNet50 + SA +BL + BC & 0.9289 \(\pm\) 0.1053 & 0.8740 \(\pm\) 0.0770 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Similarity coefficients for all models. The similarity coefficients are represented as mean \(\pm\) standard deviation. The best performing model was the ResNet34 model with a self-attention layer, bottleneck connection and blurring mechanism, which is highlighted in bold. Abbreviations: BC— bottleneck connection, BL—blurring mechanism, DSC—DSC Similarity Coefficient, JSC—Jaccard Similarity Coefficient, SA—self-attention layer, SD—standard deviation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Configuration** & **Inference Time (ms/Image)** & \begin{tabular}{c} **Batch Inference Time** \\ **(1260 Images) (s)** \\ \end{tabular}