While image classification and object recognition remain the mainstream computer vision (CV) tasks, recent frameworks also address image segmentation methods to handle more complex scenarios, such as panoptic segmentation, which merges semantic and instance segmentation to produce accurate pixel-level annotations for richer scene understanding. Panoptic segmentation poses challenges due to overlapping objects, low image quality, lack of training data, and the need for precise, pixel-level annotations. Investing in a suitable platform with pre-built segmentation frameworks and tools is crucial to efficiently label visual data of all types and formats through user-friendly interfaces. Key considerations include annotation methods, support for multi-modal data, scalability, collaboration, automation, user interface, integrability, data security, price, and the best solution for teams looking for enterprise-grade image and video annotation solutions or beginner-level segmentation tools. Leading platforms offering panoptic segmentation functionality include Encord, iMerit, Segments.ai, Kili, Superb AI, Mindkosh, SuperAnnotate, Hasty, and Labelbox, each with unique features and pricing models.