E(3)×SO(3)E(3) \times SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI

We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an $E(3)\times SO(3)$ equivariant framework for sparse deconvolution of volumes where each voxel contains a spherical signal. Such 6D data naturally arises in diffusion MRI (dMRI), a medical imaging modality widely used to measure microstructure and structural connectivity. As each dMRI voxel is typically a mixture of various overlapping structures, there is a need for blind deconvolution to recover crossing anatomical structures such as white matter tracts. Existing dMRI work takes either an iterative or deep learning approach to sparse spherical deconvolution, yet it typically does not account for relationships between neighboring measurements. This work constructs equivariant deep learning layers which respect to symmetries of spatial rotations, reflections, and translations, alongside the symmetries of voxelwise spherical rotations. As a result, RT-ESD improves on previous work across several tasks including fiber recovery on the DiSCo dataset, deconvolution-derived partial volume estimation on real-world \textit{in vivo} human brain dMRI, and improved downstream reconstruction of fiber tractograms on the Tractometer dataset. Our implementation is available at https://github.com/AxelElaldi/e3so3_convComment: Accepted to Medical Imaging with Deep Learning (MIDL) 2023. Code available at https://github.com/AxelElaldi/e3so3_conv . 19 pages with 6 figure

AnyStar: Domain randomized universal star-convex 3D instance segmentation

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM, and placental cotyledons in human fetal MRI, all without any retraining, finetuning, transfer learning, or domain adaptation. Code is available at https://github.com/neel-dey/AnyStar.Comment: Code available at https://github.com/neel-dey/AnySta

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimation methods remain time-intensive. In this preliminary work, we frame subject-specific atlas building as learning a neural field of deformable spatiotemporal observations. We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero. Our method yields high-quality atlases of fetal BOLD time-series with $\sim$5-7$\times$ faster convergence compared to existing work. While our method slightly underperforms well-tuned baselines in terms of anatomical overlap, it estimates templates significantly faster, thus enabling rapid processing and stabilization of large databases of 4D dynamic MRI acquisitions. Code is available at https://github.com/Kidrauh/neural-atlasingComment: 6 pages, 2 figures. Accepted by Medical Imaging Meets NeurIPS 202

Effects of Fe2O3 addition and annealing on the mechanical and dissolution properties of MgO- and CaO-containing phosphate glass fibres for bio-applications

This paper investigated the preparation of phosphate glass fibres (PGFs) in the following systems: i) 45P2O5-5B2O3-5Na2O-(29-x)CaO-16MgO-(x)Fe2O3and ii) 45P2O5-5B2O3-5Na2O-24CaO-(21-x)MgO-(x)Fe2O3(where x = 5, 8 and 11 mol%) for biomedical applications. Continuous fibres of 23 ± 1 μm diameter were prepared via a melt-draw spinning process. Compositions with higher Fe2O3content and higher MgO/CaO ratio required higher melting temperature and longer heating time to achieve glass melts for fibre pulling. The effects of Fe2O3 addition and annealing treatment on mechanical properties and degradation behaviours were also investigated. Adding Fe2O3 was found to increase the tensile strength from 523 ± 63 (Ca-Fe5) to 680 ± 75 MPa (Ca-Fe11), improve the tensile modulus from 72 ± 4 (Ca-Fe5) to 78 ± 3 GPa (Ca-Fe11) and decrease the degradation rate from 4.0 (Mg-Fe5) to 1.9 × 10-6kg m-2s-1(Mg-Fe11). The annealing process reduced the fibre tensile strength by 46% (Ca-Fe5), increased the modulus by 19.6% (Ca-Fe8) and decreased the degradation rate by 89.5% (Mg-Fe11) in comparison to the corresponding as-drawn fibres. Additionally, the annealing process also impeded the formation of precipitate shells and revealed coexistence of the precipitation and the pitting corrosion as fibre degradation behaviours