Megapixel image segmentation is central to high-resolution histopathology analysis, but standard pipelines depend on patching or downsampling that lose important context. MegaSeg introduces an end-to-end segmentation framework for megapixel images that combines streaming convolutional networks, a U-shaped architecture, and a divide-and-conquer strategy. It preserves fine detail and global structure while dramatically reducing memory requirements for very large images.