#segmentation

SLANT provides a deep learning solution for comprehensive segmentation of the brain into 133 labels using T1 MRI data and the BrainCOLOR protocol. Encapsulated within a Docker image, SLANT simplifies the process with pre-processing, deep learning, and post-processing integrated into a single pipeline. The project includes access to source code, documentation, and example applications for both GPU and CPU environments. This tool is designed for noncommercial use with commercial licenses available.

Explore PixelLM, a novel model for pixel-level reasoning that enhances efficiency without the need for expensive segmentation models, employing a strong architecture of a vision encoder, language model, pixel decoder, and segmentation codebook. The MUSE dataset supports thorough training and evaluation, setting new benchmark standards through effective multi-target differentiation and high-quality mask production.

This open-source project presents a unified transformer-based architecture that enhances object detection and segmentation. It supports panoptic, instance, and semantic segmentation, and demonstrates data cooperation across tasks. The project is compatible with COCO, ADE20K, and Cityscapes datasets, offering a range of pre-trained models and tools for various AI applications.

Segment Any 3D Gaussians (SAGA) is a platform for 3D object segmentation using Gaussian techniques. It provides tools like a GUI and Jupyter notebooks for both manual and automated segmentation, utilizing pre-trained models and datasets such as 360_v2 and LERF. The platform supports rendering and visualization with clustering methods like HDBSCAN.