NeSM: A NeRF-Based 3D Segmentation Methodfor Ultrasound Images

We introduce NeSM, a NeRF-based 3D segmentation method, integrating a pre-trained segmentation network's annotation maps as 2D priors through a feature-wise linear modulation (FiLM) layer. By employing this approach, the network can produce high-accuracy segmentation predictions for 3D ultrasound (US) images. NeSM outperforms existing methods, demonstrating higher pixel accuracy, dice coefficient, and mean intersection over union (mIoU) for ultrasound (US) image segmentation.