# Ultrasound Ovarian Segmentation Sample Application
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> [!NOTE]
> This application is for **reference and evaluation purposes only**. It is
**not intended for direct use in clinical or diagnostic environments** and is not
validated for such a purpose.
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The application demonstrates how the OpenVINO™ Toolkit (OpenVINO™) uses Intel hardware
acceleration (Intel iGPU, with CPU fallback) for AI-based real-time ovarian tumor segmentation
in 2D ultrasound. It offers two interchangeable models, selected with `-Arch`: a default
promptless SegFormer-B5 (DS2Net) and an optional prompted SAM-256 pipeline (YOLO bounding box
→ SAM encoder → mask decoder). An architecture that decouples capture, inference, and display
keeps the displayed video smooth regardless of inference speed, and a built-in GPU governor
enforces the KPI of keeping Intel iGPU utilization below 80%.
Supported inputs:
- Video file (for demonstrations and benchmarking)
- USB camera or webcam
- Folder with ultrasound still images (paced like a slideshow)
## Next Steps
- See [Model Preparation](./get-started/model-preparation.md) to produce the OpenVINO™
Intermediate Representation (IR) that the application loads. The application does not
include a trained model.
- See [Get Started](./get-started.md) for step-by-step setup and run instructions, including
the **web dashboard (UI)**, which is the primary way to control the application.
- See [Runtime Configuration](./runtime-configuration.md) for all flags and environment
variables that the application exposes.
- See [Troubleshooting](./troubleshooting.md) for common issues.
- See [Release Notes](./release-notes.md) for version history.
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Get Started
Runtime Configuration <./runtime-configuration.md>
Troubleshooting <./troubleshooting.md>
Release Notes <./release-notes.md>
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