NV Segment CT

--- license: other license_name: nvidia-open-model-license-agreement license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ pipeline_tag: image-segmentation library_name: monai tags: - nvidia - medical-imaging - ct - segmentation - vista3d --- # NV-Segment-CT  ## Description: NV-Segment-CT is a specialized interactive foundation model for 3D medical imaging. It excels in providing accurate and adaptable segmentation analysis across anatomies and modalities. Utilizing a multi-head architecture, NV-Segment-CT adapts to varying conditions and anatomical areas, helping guide users' annotation workflow. This model is for research purposes and not for clinical usage. **Training & Fine-tuning**: Visit [GitHub](https://github.com/NVIDIA-Medtech/NV-Segment-CTMR) for training code, fine-tuning guides, continual learning examples, and comprehensive development documentation. Core to NV-Segment-CT are three workflows: - **Segment everything**: Enables whole body exploration, crucial for understanding complex diseases affecting multiple organs and for holistic treatment planning. - **Segment using class**: Provides detailed sectional views based on specific classes, essential for targeted disease analysis or organ mapping, such as tumor identification in critical organs. - **Segment point prompts**: Enhances segmentation precision through user-directed, click-based selection. This interactive approach accelerates the creation of accurate ground-truth data, essential in medical imaging analysis. ## Github link: https://github.com/NVIDIA-Medtech/NV-Segment-CTMR ## Run pipeline: For running the pipeline, NV-Segment-CT requires at least one prompt for segmentation. It supports label prompt, which is the index of the class for automatic segmentation. It also supports point-click prompts for binary interactive segmentation. Users can provide both prompts at the same time. Here is a code snippet to showcase how to execute inference with this model. ```python import os import tempfile import torch from hugging_face_pipeline import HuggingFacePipelineHelper FILE_PATH = os.path.dirname(__file__) with tempfile.TemporaryDirectory() as tmp_dir: output_dir = os.path.join(tmp_dir, "output_dir") pipeline_helper = Huggi...