Open-source software and tools play a significant role in computer vision and medical imaging machine-learning projects, offering cost-effective solutions for tasks like image annotation and model training. Notable tools such as 3DSlicer and ITK-Snap are tailored for medical image datasets and support formats like DICOM and NIfTI. However, these tools face limitations, particularly in scaling annotation activities, ensuring robust data security, and enabling effective team collaboration, which are critical in the healthcare sector due to strict compliance requirements and the need for precise diagnostic outputs. The lack of cloud-based capabilities and audit trails in open-source tools can impede large-scale projects and compliance with regulations like HIPAA and FDA. Consequently, project leaders often seek premium solutions that offer intuitive, collaborative, and cloud-based interfaces with built-in quality control and compliance features, ensuring efficient and secure management of medical imaging data.