Nerve-Us.AI
Link to open source: https://github.com/redditard/UltrasoundNerveSegmentation
The ultrasound image segmentation model was built using TensorFlow and Keras, leveraging a modified U-Net architecture. It processes 96x96 pixel images, applying convolutional autoencoding with skip connections to preserve spatial information. The model was trained for 20 epochs (~30 seconds per epoch) using the Adam optimizer with a custom Dice coefficient loss function, achieving a Dice coefficient of 0.7. The implementation uses scikit-image for image processing, with support for Python 2.7-3.5, ensuring compatibility and easy customization.
This model can significantly aid in the medical field by enhancing diagnostic accuracy and improving treatment planning. It automates nerve identification in ultrasound images, reducing manual workload for medical professionals and accelerating patient diagnosis. The model's efficiency and precision make it ideal for large-scale medical imaging applications, where accurate segmentation is crucial for patient care.
Revenue generation is possible through licensing the model to hospitals, medical imaging centers, and telemedicine platforms. It can also be offered as a cloud-based API service for healthcare providers needing fast, reliable segmentation. Additionally, partnerships with medical device manufacturers can further monetize the technology. By continuously improving and expanding the model's capabilities, future applications may include broader medical diagnostics and personalized healthcare solutions.
This build was uploaded as a hackathon project
