## 5 Conclusions  
Precise results comparable to state-of-the-art segmentation algorithms have been achieved with the proposed segmentation strategy. The primary objective of this work was to explore the feasibility of using simple neural network architectures typically employed in semantic segmentation to achieve a reasonable approach to instance segmentation. By preprocessing the dataset to identify critical pixels separating different instances, the model was trained to prioritize these pixels, facilitating the generation of independent segmentation masks for each instance. Additionally, the proposed method does not incur any computational overhead during inference, minimally complicates model design and training, and requires straightforward post-processing of inferred segmentations to detect individual instances. The obtained results demonstrate moderately high accuracy and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **IoU** & **Precision** & **Recall** & **FPS** \\ \hline [PERSON] et al. [39] & 0.556 & - & - & - \\ [PERSON] and [PERSON] [54] & 0.814 & 0.879 & 0.919 & - \\ [PERSON] et al. [55] & 0.873 & 0.935 & 0.924 & - \\ [PERSON] et al. [56] & 0.872 & - & - & 49 [FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] \\ [PERSON] and [PERSON] [61] & 0.478 & - & - & - \\ [PERSON] et al. [57] & 0.466 & - & - & - \\ [PERSON] et al. [58] & 0.902 & 0.954 & 0.940 & 200 [2] \\ [PERSON] et al. [59] & 0.824 & - & - & 19.8 [3] \\ [PERSON] et al. [60] & 0.902 & 0.982 & 0.968 & 65.4 [4] \\ Our model & 0.804 & 0.914 & 0.872 & 43.7 [5] \\ \hline \hline \end{tabular}  
* Hardware setup: 1 NVIDIA P100 GPU, 2 NVIDIA RTX 2080 Ti GPU, 3 NVIDIA RTX 2080 Ti GPU, 4 NVIDIA GTX 1080 Ti GPU, 5 Intel i5-1500 KF CPU.  
\end{table}
Table 6: Comparison with state-of-the-art models.  
the provision of individual masks for each instance, showcasing the model's effectiveness in segmenting complex objects like hands with distinct boundaries. It has limitations regarding the temporal stability and quality of the instance information, so its viability is also application specific. Finally, the model is light-weight, which is crucial for applications such as VR, and it operates in real-time at 44 FPS. This combination of high performance and real-time capability underscores the model's effectiveness in segmenting complex objects like hands with distinct boundaries.  
Conceptualization, E.d.l.F.; methodology, M.V. and A.C.; software, M.V.; validation, M.V. and A.C.; formal analysis, M.V., A.C. and E.d.l.F.; writing--original draft preparation, M.V. and A.C.; writing--review and editing, E.d.l.F. and J.C.F.; visualization, M.V.; supervision, E.d.l.F. and J.C.F.; project administration, E.d.l.F. and J.C.F.; funding acquisition, J.C.F. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Spanish Ministry of Science, Innovation, and Universities, through the research project Shared-control strategies in Laparoscopic Liver Surgery (PID2022-138206 OB-C33).  
The EgoHands dataset used in the study was obtained from the IU Computer Vision Lab and is openly available at [[https://vision.soic.indiana.edu/projects/egohands/](https://vision.soic.indiana.edu/projects/egohands/)]([https://vision.soic.indiana.edu/projects/egohands/](https://vision.soic.indiana.edu/projects/egohands/)) [39], accessed on 15 October 2024.  
The authors declare no conflicts of interest.