SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model

We introduce SAM3D, a new approach to semi-automatic zero-shot segmentation of 3D images building on the existing Segment Anything Model. We achieve fast and accurate segmentations in 3D images with a four-step strategy comprising: volume slicing along non-orthogonal axes, efficient prompting in 3D, slice-wise inference using the pretrained SAM, and recoposition and refinement in 3D. We evaluated SAM3D performance qualitatively on an array of imaging modalities and anatomical structures and quantify performance for specific organs in body CT and tumors in brain MRI. By enabling users to create 3D segmentations of unseen data quickly and with dramatically reduced manual input, these methods have the potential to aid surgical planning and education, diagnostic imaging, and scientific research.

Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models

Deep learning methods for accelerated MRI achieve state-of-the-art results but largely ignore additional speedups possible with noncartesian sampling trajectories. To address this gap, we created a generative diffusion model-based reconstruction algorithm for multi-coil highly undersampled spiral MRI. This model uses conditioning during training as well as frequency-based guidance to ensure consistency between images and measurements. Evaluated on retrospective data, we show high quality (structural similarity > 0.87) in reconstructed images with ultrafast scan times (0.02 seconds for a 2D image). We use this algorithm to identify a set of optimal variable-density spiral trajectories and show large improvements in image quality compared to conventional reconstruction using the non-uniform fast Fourier transform. By combining efficient spiral sampling trajectories, multicoil imaging, and deep learning reconstruction, these methods could enable the extremely high acceleration factors needed for real-time 3D imaging.