# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 5 Conclusions  
In this study, the performance of a state-of-the-art segmentation model, based on U-Net [4] is first analyzed by using CT-Scans of different organs, namely liver, bones and portal vein. Our results show that the standard U-Net network can provide very good results for segmenting large organs, approaching 98% Accuracy values in terms of DSC metric, but it exhibits poor performance when organ images characterized by small and sparse segmentation training masks are used. In particular, this happened in the case of vascularization images. In fact, segmentation of small organs is a recurring and challenging issue for automated medical Image Segmentation [22; 34; 35]. In order to overcome this problem, a novel approach producing a significant improvement of the DSC metric in the most critical cases is proposed. This approach does not require the use of additional data samples, nor a significant additional computational burden. In fact, for all our analyses, the free computing infrastructure made available by Google Colab was enough.  
In particular, the suitable working conditions of the baseline U-Net are determined for segmenting sparse and/or small sections through image pre-processing. Since a quite general approach and metric (the DSC) to drive the segmentation is used, the solution can be used in other situations based on similar images and metrics. In the most critical case, relevant to portal vein images, the DSC improvement obtained is 20%. This comes basically at a very small cost, leaving untouched the training time and computing requirements in comparison with the baseline U-Net processing.  
Our approach is based on training the U-Net by using differentiated zoom levels in different areas of test images. Thus, it follows that resorting to higher resolution images can bring further significant benefits in terms of segmentation accuracy. Clearly, an increase in resolution would require additional storage space and computing power. However, determination of the achievable performance improvement, if anything, needs further research. In fact, the observed improvement is due to the usage of the Dice metric and the impact of additional resolution, and a higher scaling factor on images cannot be easily determined; a deep investigation is necessary.  
Due to its flexibility and small computational cost, the application of differentiated zoom levels, associated with the Dice metric, used to enhance the segmentation quality of small entities, can potentially be applied to other use-cases characterized by sparse segmentation signals. Nevertheless, the application of the proposed approach to other use-cases is beyond the scope of this paper. Finally, the proposed approach can have a significant impact in operation, since a correct segmentation of a vasculature or any other small organs and disease areas is essential for helping medical diagnosis.  
Conceptualization, methodology and validation, G.R.; software, G.R. and P.B.; writing--original draft preparation, P.B. and M.F.; writing--review and editing, M.F. and G.R.; supervision, G.R.; funding acquisition, G.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the company Molecular Horizon srl to the Department of Engineering of the University of Perugia.  
Not applicable.  
This research has been conducted on the publicy availabe Liver segmentation 3D-IRCADb-01 from IRCAD institute, available at [[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).  
The authors declare no conflict of interest.