SVRDA: A Web-based Dataset Annotation Tool for Slice-to-Volume
  Registration
Weixun Luo Alexandre Triay Bagur Paul Aljabar George Ralli Sir Michael Brady
Background and Objective: The lack of benchmark datasets has impeded the
development of slice-to-volume registration algorithms. Such datasets are
difficult to annotate, primarily due to the dimensional difference within data
and the dearth of task-specific software. We aim to develop a user-friendly
tool to streamline dataset annotation for slice-to-volume registration.
  Methods: The proposed tool, named SVRDA, is an installation-free web
application for platform-agnostic collaborative dataset annotation. It enables
efficient transformation manipulation via keyboard shortcuts and smooth case
transitions with auto-saving. SVRDA supports configuration-based data loading
and adheres to the separation of concerns, offering great flexibility and
extensibility for future research. Various supplementary features have been
implemented to facilitate slice-to-volume registration.
  Results: We validated the effectiveness of SVRDA by indirectly evaluating the
post-registration segmentation quality on UK Biobank data, observing a dramatic
overall improvement (24.02% in the Dice Similarity Coefficient and 48.93% in
the 95th percentile Hausdorff distance, respectively) supported by highly
statistically significant evidence ($p<0.001$).We further showcased the
clinical usage of SVRDA by integrating it into test-retest T1 quantification on
in-house magnetic resonance images, leading to more consistent results after
registration.
  Conclusions: SVRDA can facilitate collaborative annotation of benchmark
datasets while being potentially applicable to other pipelines incorporating
slice-to-volume registration. Full source code and documentation are available
at https://github.com/Roldbach/SVRDA