RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans

Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing valuable contextual information. As an alternative to requiring costly and complex 3D ultrasound scanners, 3D volumes can be constructed from 2D scans using machine learning. However this usually requires long computational time. Here, we propose RapidVol: a neural representation framework to speed up slice-to-volume ultrasound reconstruction. We use tensor-rank decomposition, to decompose the typical 3D volume into sets of tri-planes, and store those instead, as well as a small neural network. A set of 2D ultrasound scans, with their ground truth (or estimated) 3D position and orientation (pose) is all that is required to form a complete 3D reconstruction. Reconstructions are formed from real fetal brain scans, and then evaluated by requesting novel cross-sectional views. When compared to prior approaches based on fully implicit representation (e.g. neural radiance fields), our method is over 3x quicker, 46% more accurate, and if given inaccurate poses is more robust. Further speed-up is also possible by reconstructing from a structural prior rather than from scratch.

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations

Accurate topology is key when performing meaningful anatomical segmentations, however, it is often overlooked in traditional deep learning methods. In this work we propose TEDS-Net: a novel segmentation method that guarantees accurate topology. Our method is built upon a continuous diffeomorphic framework, which enforces topology preservation. However, in practice, diffeomorphic fields are represented using a finite number of parameters and sampled using methods such as linear interpolation, violating the theoretical guarantees. We therefore introduce additional modifications to more strictly enforce it. Our network learns how to warp a binary prior, with the desired topological characteristics, to complete the segmentation task. We tested our method on myocardium segmentation from an open-source 2D heart dataset. TEDS-Net preserved topology in 100% of the cases, compared to 90% from the U-Net, without sacrificing on Hausdorff Distance or Dice performance. Code will be made available at: www.github.com/mwyburd/TEDS-Net