# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 4 Performance Evaluation
The proposed network operates with two levels, a coarse-segmentation stage and a fine-segmentation stage, with the introduction of a saliency transformation module. This module converts the previous iteration's segmentation probability map into spatial weights for the current iteration. The proposed model produces a DSC of 84.5% on the pancreas NIH dataset. Hence, the performance obtained with the approach presented in this paper is comparable with the state-of-the-art, without requiring additional complexity in the network, increasing neither the computational cost nor the needed volume of training data. Figure 15: Flow diagram of the training of the mixed size medical segmentation procedure via U-Net. In summary, with the proposed approach, the segmentation of binary masks with sparse signals, such as in the case of vascularization or small organ scans, is improved. In particular, an increased accuracy evaluated through the DSC metric is of great importance in the medical field, especially for vascularization images. In fact, the analysis of these images is fundamental to accelerating the diagnosis of various cancer types, due to the tumor angiogenesis process [5]. It is worth stressing that the achieved performance is reached with the base version of Google Colab, without the need for additional, paid resources. This is an important result, since it demonstrates that testing with U-Net can be carried out easily in a standard developing environment available to everyone, without requiring one to set up a dedicated cluster with multi-core CPUs or GPUs. Thus, the entry level of this approach is represented only by the skill of the programmers, without infrastructure costs. The price to pay is a small variability in execution times, due to the fact that resources in Google Colab are not reserved exclusively for free users and the fact that activity cannot last more than 12 consecutive hours. However, this is definitely acceptable for a developing work. Obviously, it is possible to set up a local cluster offering the Jupyter Notebook service or pay for dedicated cloud resources in order to not experience the limitations of the free account, but this is more suitable in operation, when service discontinuity is not acceptable. In any case, the software to run is exactly the same developed on the free version of Google Colab. Finally, it is interesting to evaluate the time taken by the overall procedure to complete the test phase, i.e., in operation. The results of the evaluation campaign are reported in Table 3. The evaluation included four cases, each one repeated several times to evaluate both mean time and standard deviation, as follows: (i) single image; (ii) set of images of a single patient, with 129 slices; (iii) set of images of a single patient, with 172 slices; (iv) set of images of a single patient, with 200 slices. The usage of the dataset of different patients, each with a different number of slices, allows evaluating the scalability of the procedure. The used images are already loaded in Google Colab, to avoid including the effects of the upload time in the evaluation, since it depends on external factors, such as speed and reliability of network connection. It results that the segmentation of a single image taken from a batch of the test set by Tensorflow requires 3.286 s on average. In addition, in cases in which the zoom of images is not necessary, the initial pre-processing (rescaling operation) requires a mean time of 21 ms. In cases in which the zooming procedure is completed, it takes a slightly higher mean time, equal to 23 ms. Both of them are negligible with respect to the processing time. When the processing is executed on a larger set of images, processed in batches of 10 images, both processing and pre-processing times scale very well. In fact, they exhibit a sub-linear behaviour, with a processing time for 200 images well below 20 s and pre-processing always below 100 ms, with an impact of zooming operation that is essentially negligible.