## 6 Conclusions and Future Work  
This paper presents the methods that utilize YOLOv4 combined with U-Net for automatic prostate segmentation. The models were trained ab initio on onefold (MRI) and joint multimodal images (MRI mixed and CT). The most accurate model resulted in training with joint (mixed) modalities. The Dice coefficients reported in the publications mentioned in the Section 2 range from 91.4% to 99%, with the highest values achieved using data fusion of T2-weighted and diffusion-weighted MR imaging, which we did not employ. The Dice coefficient achieved by our best model, exceeding 96%, falls within this range, demonstrating competitive performance compared to existing literature. However, it is important to note that the aforementioned models were evaluated on different datasets, often using various imaging modalities, so the results are not directly comparable. That result may significantly impact the dialogue on the adoption of deep learning-based methods and applications in healthcare and may remove one of the barriers defined as operational costs. Deploying and maintaining deep learning models requires a specific infrastructure, which impacts costs. The number of models increases cost linearly. Having a model that performs an efficient analysis on two modalities without losing the accuracy removes that constraint. Furthermore, the model learns on CT images and can effectively identify the prostate in MRI, which is a less invasive medical examination. Successful models trained on multimodal medical images reduce the need for CT scans and the risk of radiation-induced prostate cancer. The next step of the research is to detect primary prostate tumors located within the prostate organ, which is shown in Figure 12. That milestone will significantly support radiologists and medical experts in making decisions, as detecting primary prostate tumors is a long-term and difficult task. The barriers to this step are the kinds of data and detailed labels on diverse and numerous slices. This paper [32] shows promising results in this direction for MRI data; however, further methods aimed at improving the Dice coefficient can still be developed, as well as algorithms for detecting tumors in CT modalities. The described methods can be applied as a baseline to the enumerated next step, but also to the detection and segmentation of other organs and tumors such as brain tumors or the detection of metastases. **Author Contributions:** Conceptualization, T.L. and K.S. ; investigation, E.K. and K.S. ; methodology, E.K. ; project administration, K.S. ; resources, E.K. ; software, Z.K.-B. and A.V. ; supervision, K.S. ; validation, E.K. and T.L. ; visualization, Z.K.-B. ; writing--review and editing, T.L., K.S. and Z.K.-B. All authors have read and agreed to the published version of the manuscript. Figure 12: Primary prostate tumors (pink color) within segmented prostate organ (orange color). Funding: This research received no external funding. Institutional Review Board Statement: The data used in our study were collected from an existing dataset provided by a medical institution. The dataset was obtained ethically, with all necessary approvals, and was fully anonymized prior to our use. Importantly, our research did not involve collecting new patient data or conducting additional clinical studies. Informed Consent Statement: Not applicable. The data used in this study are anonymized, and the entity that collected the data has the legal right to distribute them for research purposes. No identifiable human subjects were involved in the study. Data Availability Statement: The datasets presented in this article are not readily available because they are subject to medical confidentiality and contain sensitive patient information. Access to the data is restricted to hospital employees and authorized collaborators in accordance with legal and ethical guidelines. Requests to access the datasets should be directed to corresponding author. Conflicts of Interest: The authors declare no conflicts of interest.