# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 4 Performance Evaluation
A preprocessing step, consisting of magnifying the portion of interest of the binary masks and of the corresponding areas in slice images, is introduced as follows:  
* Zooming the image and the corresponding mask in the target area of vascularization, in order to increase the size of the vascularization segments. The zoom was configured to enhance the body section of interest, by cropping with the Tensorflow function tf.image.central_crop() up to 70% of the original image. Hence, in this case, the resulting image is reduced to a size of 154 \(\times\) 154 pixels from the original size of 512 \(\times\) 512 pixels. This resulted in the optimal percentage for the best DSC metric, found experimentally, as shown in Table 2.
* Resizing the samples to the 128 \(\times\) 128 format, as in the previous experiments. The rationale of this strategy is that the higher resolution of original images is leveraged to provide the U-Net with additional information, in order to enlarge the target area. In fact, with the original setting, the amount of the target area in the mask is too small to drive the Dice metric towards a correct recognition of it. This is due to the metric definition itself, which is based on the percentage amount of pixels of the target image (see (3)). This causes, as mentioned in the comments to Figures 8 and 9, the missing recognition of small, sparse spots of the vascularization. Although the resolution in the original setting cannot be handled by the available computing resources, it offers the opportunity to gain additional information via zooming the interested portion of the image before resolution rescaling. The advantages are multiple: not only is the information overlooked in base configuration (i.e., the one without any zoom) leveraged, but the training time and the amount of necessary computing resources are kept unmodified. After pre-processing, a similar U-Net was trained by using the new images for 90 iterations. The new model leads to an increase in the DSC metric up to 22% for portal veins, corresponding to 81.45% on the test set with the largest and optimal zoom level. Figures 10 and 11 depict two sample outputs obtained by using the new model, specially tuned to segment vascularization in medical CT scans. The positive impact of preprocessing can also be observed on the predicted mask quality. Even in the presence of sparse signals, the model is good at detecting all the present segments, although some minor discrepan \begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **DSC** \\ \hline Base configuration (no zoom) & 58.53\% \\ \hline
40\% image cropping & 62.04\% \\
50\% image cropping & 65.23\% \\
60\% image cropping & 76.39\% \\
70\% image cropping & 81.45\% \\ \hline Weighted cross entropy loss & 27.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DSC metric for different percentages of cropping of the original images (portal vein). cies in the vascularization segment shapes (orange box) are present (Figures 10c and 11c). Nevertheless, through the proposed approach, the model is able to fetch significantly small segments that were previously ignored, as shown in another sample in Figure 12 on the green box on the right side of the binary mask (Figure 12c). This is further confirmed by the sample in Figure 13, replicating the same two distinct segments highlighted in the green box (Figure 13c). Although results are appreciable, some margin for further improvement exists. In particular, some research is still necessary to refine the model in order to identify even the smallest segments, such as the ones missing in the prediction output of the sample shown in Figure 13c. This cannot be done by further zooming the available images, thus different techniques have to be designed in the future, or images with higher resolution are necessary. Figure 11: Portal vein sample 4: image (**a**), original mask (**b**) and predicted mask (**c**) after pre-processing with zoom.