# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 3 Dataset, Model and Parameters Configuration  
### Dataset  
This paper shows the performance achievable by using the U-Net architecture for medical image segmentation during both the training and test phases. For this purpose, a publicly available dataset, the 3_Dircadb1_ dataset ([[https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01/](https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01/)]([https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01/](https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01/)), accessed on 1 November 2022), was used. It includes 3D CT-Scan images of 10 women and 10 men with liver tumors in 75% of cases. The dataset is anonymized. For each patient, it includes masks of different organs. For our analysis the images of three different organs available in the dataset have been selected. They are characterized by very different shape, size, compactness and mask features: liver, bone and portal vein. The difference between these organs and relevant masks allows performing a multi-scale and multi-shape analysis of the achievable U-Net performance. In fact, while liver binary masks show a considerable amount of information (for instance, with reference to the slice in Figure 2, see the white portion in Figure 3a), in case of bone and portal vein, a gradual and significant decrease of information appear in their masks. In such situations, the shortage of the available white pixels, representing the information needed for training the network, limits the effectiveness of the segmentation process. Thus, processing images of these different organs allowed us not only to test the effectiveness of the U-Net, but also to introduce simple yet effective strategies to overcome such limitations.  
The selected three organ images are provided for all the patients of the dataset. Each patient's folder includes four subfolders, in which the DICOM-formatted images, the labelled images, the mask images and the surface meshes can be found.  
All images are of the same size, equal to 512 \(\times\) 512 pixels. To process the dataset, 20 folders have been used, each one corresponding to a different patient. These folders are called '_3 Dircadb1.number_', with _number_ varying between 01 and 20 and each of them, containing the following four subfolders:  
* '_PATIENT_DICOM_': The patient's images in DICOM format. Figure 2a shows an example of a complete CT-Scan slice, with relevant frequency of HU values reported in the companion histogram of Figure 2b;
* '_MASK_DICOM_': A set of subfolders corresponding to the names of the various segmented organs, containing the binary DICOM image of each original mask. Figure 3a shows an example of a liver mask, with the relevant frequency of HU values (corresponding to black and white in this case) in the histogram of Figure 3b. In this subfolder, the segmentation masks of liver, bone and portal vein used in this work can be found for each patient;
* '_LABELLED_DICOM_': The ensemble of segmented images corresponding to all the patient's analyzed organs, including the ones considered in this work (liver, bone and portal vein), in DICOM format. Figure 4a shows an example of a multi-segmented  
Figure 4: Multi segmented mask plot from ‘_LABELLED_DICOM_’ (**a**) and corresponding histogram of HU values (**b**).  
Figure 3: Binary mask plot from ‘_MASK_DICOM_’ (**a**) and corresponding histogram of HU values (**b**).  
Figure 2: CT-Scan plot from ‘_PATIENT_DICOM_’ (**a**) and corresponding histogram of HU values (**b**).  
mask, with relevant frequency of HU values (one for each mask) shown in a histogram in Figure 4b;
* _'MESHES_VTK'_: All the files corresponding to the surface meshes of the various areas of interest in Visualization Toolkit (VTK) format.