## 5 Discussion
### Requirements for Training Segmentation Models  
As noted in Section 1, training segmentation models for medical imaging demands substantial computational resources, specialized expertise, and significant financial investment. Recent studies have highlighted these challenges. For instance, DMDF-Net [19], a dual multiscale dilated fusion network, was designed for segmenting COVID-19 lesions in lung CT scans. The model was trained on the MosMed [41] and COVID-19-CT-Seg [18] datasets (70 CT scans totaling 5569 images). Training was conducted over 15 epochs with a batch size of eight and a learning rate of 0.001, utilizing an NVIDIA GTX 1070 GPU. The total estimated project cost, covering dataset curation, model architecture design, model training and evaluation, and personnel expenses, including three engineers, amounted to EUR 61,500 over six months. This estimate is based on the average annual salary of a Machine Learning Engineer in Spain, approximately EUR 41,000 ([[https://www.glassdoor.es/Salaries/Spain-machine-learning-engineer-salary-SRCH_IL_0_5_IN219_KO6,31.htm?countryRedirect=true](https://www.glassdoor.es/Salaries/Spain-machine-learning-engineer-salary-SRCH_IL_0_5_IN219_KO6,31.htm?countryRedirect=true)]([https://www.glassdoor.es/Salaries/Spain-machine-learning-engineer-salary-SRCH_IL_0_5_IN219_KO6,31.htm?countryRedirect=true](https://www.glassdoor.es/Salaries/Spain-machine-learning-engineer-salary-SRCH_IL_0_5_IN219_KO6,31.htm?countryRedirect=true)), accessed on 12 March 2025). Similarly, TransAttUnet [21], a multi-level attention-guided U-Net incorporating transformer-based mechanisms, was developed for medical image segmentation. The model was trained on 4255 images from the ISIC-2018 [42], JSRT [43], Montgomery [20], NIH [44], Clean-CC-CCII [45], Bowl [46], and GLa5 [47] datasets. Training was conducted over 100 epochs with a batch size of four and a learning rate of 0.0001, utilizing an NVIDIA Titan XP GPU. Given that this project required a team of five engineers, the estimated cost amounted to EUR 102,500 over six months, using the same salary reference. Another example is a Mask R-CNN-based approach [26] for lung CT segmentation, trained on a dataset of 1265 ground-truth images. The model was trained using an NVIDIA GTX 1050 Ti GPU. The total expenditure for this project amounted to 101,600 \(\copyright\) over six months, underscoring the substantial costs associated with prolonged training and hyperparameter optimization. Assuming a team of four engineers, personnel costs alone would be approximately EUR 82,000, according to the previously cited salary benchmark. Furthermore, DS-TransUNet [48], a Residual U-Net designed for medical image segmentation, was trained on the LUNA, VESSEL12 [49], and HUG-ILD [50] datasets (11,325 images). Training was conducted over 50 epochs with a batch size of eight and a learning rate of 0.0001, utilizing an NVIDIA GTX 1060 GPU. This project involved six engineers, resulting in an estimated personnel cost of EUR 123,000 over six months, following the salary estimate previously discussed. Moreover, the time required to achieve functional results is considerable. Training and refining foundational models, such as SAM2, demands a large number of GPUs and extensive datasets to achieve optimal performance. For instance, MedSAM2, the SAM2 fine-tuned model for medical applications, utilized 64 NVIDIA A100 GPUs for distributed training [17]. This setup highlights the challenges in resource allocation and underscores the necessity of optimizing efficiency in segmentation models. These studies highlight the substantial computational demands, dataset requirements, and financial commitments associated with training segmentation models. In contrast, our proposed approach leverages pretrained segmentation models alongside advanced prompting strategies, significantly reducing both computational and development costs.