SVRDA: A Web-based Dataset Annotation Tool for Slice-to-Volume Registration

• URL: http://arxiv.org/abs/2311.15536v1
• Date: Mon, 27 Nov 2023 04:49:24 GMT
• Title: SVRDA: A Web-based Dataset Annotation Tool for Slice-to-Volume Registration
• Authors: Weixun Luo, Alexandre Triay Bagur, Paul Aljabar, George Ralli, Sir Michael Brady
• Abstract summary: 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. Various supplementary features have been implemented to facilitate slice-to-volume registration.
• Score: 42.03033994348234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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