# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 4 Performance Evaluation  
The used processing environment is the well known Google Colab ([[https://colab.research.google.com/](https://colab.research.google.com/)]([https://colab.research.google.com/](https://colab.research.google.com/)), accessed on 1 November 2022). This tool allows easy repeatability of experiments and code sharing. The free version of Google Colab was used. It offers remote execution on virtual machines (VMs) with limited lifetime (12 h) [38]. The available computing resources for each VM are 12 GB of RAM, 78 GB of disk space and GPU-accelerated computing on Nvidia Tesla K80 [39]. The Tesla K80 GPU combines two graphics processors to increase performance. It is characterized by 2496 shading units, 208 texture mapping units and 48 render output units per GPU. The comparison done in this work focused on three different organs, with very different features: liver, bone and portal vein. Each patient of the considered dataset contains segmentation images for each of these three organs. For each organ, a target U-Net was trained and tested. From the original set of 20 patients, 3 patients were discarded due to anomalies in the images that could negatively influence the training process. Figure 5 shows two examples of altered CT-Scans from _3 Dircadb1_. In the figure, it is possible to see the presence of an additional circular area that encloses the body section. This area alters the sample's HU values, blurring the difference among the body section and the external area. Hence, these images can increase the probability of unreliable segmentation in the model. During the data processing phase presented in Section 3, the CT-Scan images were resized to 128 \(\times\) 128 pixels. This choice was motivated by the availability of limited computing resources in the Colab VM. In fact, the training of the considered U-Net with images of the original sizes (512 \(\times\) 512 pixels) or on a 256 \(\times\) 256 resized version is not feasible in Google Colab, leading to memory error even when considering strongly limited batches (5 or 10 images for a batch). Hence, our training phase consisted of 90 iterations for each organ on 80% of the dataset, processing 128 \(\times\) 128 CT-Scan images in batches of 32 images. The CT-Scan images are sent as input to the network after a shuffling step. This procedure increases the robustness of the network to both image variability and overfitting. Processing of randomly sorted images is common in Image Segmentation tasks [20; 24; 40]. As mentioned above, the U-Net was implemented by Keras and Tensorflow, with the following configuration, which is referred to as 'base configuration' in this paper:  
* Optimizer: Adam [30], learning rate \(1\times 10^{-3}\). * Performance evaluation metric: Dice coefficient [6]. The first comparison was carried out with the same configuration for the three organs. The U-Net with the configuration described in the previous section provides the results  
Figure 5: Two different samples of altered CT-Scans from the _3 Dircadb1_ dataset: presence of an additional grey circular area that encloses the body section, marked by the word ‘artifact’ to better highlight it. reported in Table 1, tested on the remaining 20% of the samples, consisting of 489 images. The results are expressed in terms of DSC and Accuracy [7], defined as in (4). Nevertheless, due to the nature of the problem, the simple Accuracy metric provides unreliable results. \[Accuracy=\frac{\mid X\cap Y\mid+\mid\overline{X}\cap\overline{Y}\mid}{\mid X \mid+\mid\overline{X}\mid}. \tag{4}\]  
In fact, while the DSC decreases with the increasing sparsity of the binary masks, as expected, the corresponding increasing imbalance between 0 s and 1 s in the masks leads to a very high Accuracy. Hence, this high  
As shown in Figure 6, the network produces good results (97.85%) for the liver on the test set. The overall parenchyma tissue is well defined in the predicted mask (Figure 6c), with a good precision in the segment border.