# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 4 Performance Evaluation
Figure 12: Portal vein sample 5: image (**a**), original mask (**b**) and predicted mask (**c**) after pre-processing with zoom. Figure 10: Portal vein sample 3: image (**a**), original mask (**b**) and predicted mask (**c**) after pre-processing with zoom. The optimal zoom level was found by testing different configurations, as shown in Table 2. Figure 14 shows the trend of the DSC metric as a function of the zoom level. The saturation effect is evident for values higher than a zoom level of 3\(\times\). This effect can be explained by the fact that the maximum lossless zoom that can be performed on these images is 4\(\times\), since the original resolution of the images is 512 \(\times\) 512 and the target one used to feed the U-Net is 128 \(\times\) 128. Beyond this zoom value, the cropped image will have a size inferior to 128 \(\times\) 128; thus, further improvements for larger magnification levels are not expected. In this case, it is not possible to use a zoom value of 4\(\times\), since this would also crop part of the white portion of the mask. In addition, the figure shows that the proposed zoom procedure does not impact the Accuracy metric, which remains stable in the range 98-99%, as expected. In fact, our procedure does not involve any loss of useful data for the mask of the considered organ; thus, the Accuracy is not affected by it. Another evolutionary step is in the automatization of the whole process. In the current approach, the area of interest must be first identified by the medical staff. Then, the automatic zoom on the selected area is performed on the CT Scans and the corresponding masks, obtaining images in a 128 \(\times\) 128 format. Although most of the operations are already automatic, it would be possible to leverage the first phase in order to identify the area deserving more attention and using the first output to automatically focus the zoom operation on the right portion of the image. This will be an objective of future  
Figure 14: DSC and Accuracy metrics as a function of the zoom level. Figure 13: Portal vein sample 6: image (**a**), original mask (**b**) and predicted mask (**c**) after pre-processing with zoom. investigations. In this regard, Figure 15 presents a general overview of the segmentation procedure detailed in this section. After the preliminary image preprocessing, consisting in the rescaling to a resolution of 128 \(\times\) 128, the U-Net is trained on a portion of the dataset. The performance is then evaluated on the remainder of the samples. In the presence of small organs, as shown in what follows, a significant DSC decrease can be observed. Hence, if this decrease produces DSC values lower than a predefined threshold, corresponding to an 80% decrease in this paper, samples are further processed with an incremental zoom of images. This procedure stops in cases of data loss, i.e., if part of the organ signal is cut away by the zoom, or when the U-Net is able to achieve the desired performance score in terms of DSC. Small organ segmentation has been previously investigated in other papers, such as [34; 35]. FocusNetv2, presented in [34], was developed to segment small organs in the head and neck (HaN) area. This network is composed of various parts. They include a main segmentation network based on Snet, a U-Net variant, a small-organ localization network, to localize the center locations of small organs and a small-organ segmentation network, which refines the Snet's segmentation with the information provided by the small-organ localization network. The proposed ensemble is tested on various organs of different size, providing a maximum DSC of 82.45% on small organs. Hence, the performance obtained with our approach is comparable with the DSC obtained with more complex and computationally expensive models, at a reduced cost. A modular network is used in [35] as well, to segment pancreas images, a small organ, from the NIH dataset.