Research map / 3D vision / 3D generation
DeepBranchTracer: A Generally-Applicable Approach to Curvilinear Structure Reconstruction Using Multi-Feature Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Introduces DeepBranchTracer, a U-Net-based network (CSFL) predicting centerline, boundary, direction and radius maps plus a multi-feature iterative tracing strategy (MFT) for 2D and 3D curvilinear structure reconstruction.
Problem
Reconstructing curvilinear structures (roads, vessels, neuron branches) from images is hard due to complex topology, adjacent/tangled branches and ambiguous evidence; segmentation methods merge adjacent branches and reconstruction methods rely on hand-crafted geometric models.
Approach
Formulates curvilinear extraction as geometric attribute estimation (direction, radius) learned by a deep network with LSTM sequence modules, combined with centerline and boundary image features in an iterative local tracing procedure that needs no hand-crafted tubular/ray models.
Key results
- On fMOST-VTA, DBT reaches SSD-F1 0.910 and Length-F1 0.911, versus 0.901 SSD-F1 for neuTube+V-Net and 0.759 for SPE.
- On DRIVE, DBT achieves SSD-F1 0.893 vs. 0.857 for the best segmentation baseline (FR-Net), but with lower precision (0.827).
- On the Massachusetts Roads dataset, DBT reaches Length-F1 0.816 and ABL 200.2, above the best segmentation baseline Length-F1 0.803 (V-Net).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add geometric features to centerline-only tracing (ablation) | DeepBranchTracer on fMOST-VTA · fMOST-VTA | ABL: 128.65 -> 167.51 | Helped |
| Add the LSTM sequence learning module to geometric-only tracing (ablation) | DeepBranchTracer on fMOST-VTA · fMOST-VTA | ABL: 48.07 -> 57.04 | Helped |
| Add image and geometric features with LSTM instead of centerline features only (ablation) | DeepBranchTracer on fMOST-VTA · fMOST-VTA | SSD-F1: 0.845 -> 0.910 | Helped |
| Use centerline-only tracing instead of geometric-only tracing (ablation) | DeepBranchTracer on fMOST-VTA · fMOST-VTA | PE: 1.879 -> 0.835 | Helped |
| Add geometry features to centerline-only tracing without LSTM (ablation) | DeepBranchTracer on fMOST-VTA · fMOST-VTA | Length-F1: 0.893 -> 0.890 | No clear effect |
| Use DeepBranchTracer rather than V-Net | Curvilinear structure reconstruction · Massachusetts Roads Dataset | Length-F1: 0.803 -> 0.816 | Helped |
| Use DeepBranchTracer rather than FR-Net | Curvilinear structure reconstruction · DRIVE | SSD-F1: 0.857 -> 0.893 | Helped |
| Use DeepBranchTracer rather than FR-Net | Curvilinear structure reconstruction · DRIVE | Length-F1: 0.831 -> 0.828 | Hurt |
| Use DeepBranchTracer rather than FR-Net | Curvilinear structure reconstruction · CHASE_DB1 | Length-F1: 0.829 -> 0.845 | Helped |
| Use DeepBranchTracer rather than neuTube+V-Net | Curvilinear structure reconstruction · fMOST-VTA | ABL: 216.3 -> 198.3 | Hurt |
| Use DeepBranchTracer rather than SPE | Curvilinear structure reconstruction · DIADEM-CCF | SSD-F1: 0.379 -> 0.421 | Helped |
Surprising findings
- DBT has the highest recall but low precision on vessel datasets, which the authors attribute to increased tolerance of false positives.
- Segmentation baseline neuTube+V-Net achieves higher ABL (216.3) than DBT (198.3) on fMOST-VTA.
Limitations
- Precision is low on vessel datasets (DRIVE 0.827, CHASE_DB1 0.827) due to false positives.
- Relies on fixed thresholds T_c and T_b and tracing hyperparameters tuned per setup.
- Training is split into stages (backbone/centerline/boundary first, then direction/radius blocks frozen parts), adding pipeline complexity.
Experimental setup
Datasets: Massachusetts Roads Dataset DRIVE CHASE_DB1 DIADEM-CCF fMOST-VTA
Benchmarks: Massachusetts Roads Dataset DRIVE CHASE_DB1 fMOST-VTA DIADEM-CCF
Metrics: SSD-F1 Precision Recall Length-F1 ABL PE RE
Baselines: U-Net V-Net AU-Net CS-Net RCNN-UNet SGCN RoadTracer Vec-road FR-Net SA-Net neuTube MOST
Builds on: U-Net RoadTracer SPE
Closest papers by meaning
- Beyond the Pixel-Wise Loss for Topology-Aware Delineation (CVPR 2018 · similarity 0.86)
- Dual-level Adapter Boosting Prompt-free Curvilinear Structure Segmentation (CVPR 2026 · similarity 0.85)
- Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection (CVPR 2025 · similarity 0.83)
- VGGSfM: Visual Geometry Grounded Deep Structure From Motion (CVPR 2024 · similarity 0.82)
- VGGT: Visual Geometry Grounded Transformer (CVPR 2025 · similarity 0.82)
- STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds (CVPR 2025 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.