## 5 Conclusions  
The proposed segmentation method demonstrated its efficiency in identifying image parasites, presenting a series of significant advantages. Firstly, the results obtained were promising, allowing a precise segmentation of parasites in medical images. This enables a rapid diagnosis of the disease, facilitating adequate treatment and reducing patient risks.  
This research shows that the detection method can screen patients with VL using image analysis, especially considering that manual annotation of medical images is laborious and error-prone. The proposed method can automate this process.  
This detection method based on image segmentation can help healthcare professionals diagnose patients and identify VL parasites, as it reduces manual workload and minimizes errors associated with manual annotation. However, it is important to emphasize that a challenge faced by this method is the need for more human VL images available for training and validation. Obtaining a broader and more diverse dataset is essential to improve and  
Figure 9: Example of images with imprecise annotations of the parasites region. Original clipping on the (**left**); Ground truth in the (**center**); Predicted label on the (**right**).  
validate the method, ensuring its viability in real environments and providing a solid foundation for successful clinical implementation.  
Implementing this technique has the potential to optimize technicians' time and significantly expedite the diagnostic process. By relying on the machine's findings, laboratory personnel can confirm the diagnosis more efficiently, eliminating most of the time-consuming manual inspection of slides to identify parasites. This streamlined approach would result in faster diagnoses, ultimately saving valuable time for laboratory personnel and improving overall efficiency in the diagnostic workflow.  
Our approach was tailored based on the unique image dataset of confirmed visceral leishmaniasisics cases, allowing us to optimize the methodology for this particular disease. However, we acknowledge that there is potential for future testing of the methodology with other diseases.  
Finally, as future work, we intend to add a post-processing task in parasite segmentation to remove false positives; create a minimum viable product to present to clinical analysis laboratories. Furthermore, we intend to use a larger dataset to verify the efficiency of DL in detecting VL parasites in humans.  
Conceptualization: C.G., B.A. and R.S.; Methodology: C.G., A.B., V.D., J.M. and R.S.; Writing: C.G., A.B., V.D., J.M. and R.S.; Formal analysis and investigation: C.G. and R.S.; Funding acquisition: R.S.; Resources: B.A. and C.C.; Supervision: R.S. All authors have read and agreed to the submited version of the manuscript.  
Foundation for Research Support of Piaui (FAPEPI-Notice 02/2021). The National Council for Scientific and Technological Development (CNPq).  
Ethical approval CEP/CONEP 0116/2005.  
Not applicable.  
All source codes used in this work are available in [[https://github.com/pavic-ufpi/Leishmania_Segmentation](https://github.com/pavic-ufpi/Leishmania_Segmentation)]([https://github.com/pavic-ufpi/Leishmania_Segmentation](https://github.com/pavic-ufpi/Leishmania_Segmentation)), accessed on 6 July 2023.  
We thank the laboratory LABLEISH where we obtained the samples.  
The authors declare no conflict of interest.