mlsanityData to 7 Oct 2026

Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge

arXiv 2304.03708 ↗First posted 7 Apr 2023eess.IV cs.CVcited by 26

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Abstract (arXiv)

Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first Pulmonary ARtery SEgmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at urlhttps://parse2022.grand-challenge.org/Parse2022/.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 7 Apr 2023.

Builds on

9 of 25 references.

References · 9

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  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
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  5. 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 286
  6. 2019The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challengecited by 188
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 10; 2026: 6.

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  1. 2024GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AIcited by 96
  2. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 60
  3. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  4. 2025nnInteractive: Redefining 3D Promptable Segmentationcited by 39
  5. 2024Large-Scale 3D Medical Image Pre-training with Geometric Context Priorscited by 34
  6. 2023Topology-Aware Uncertainty for Image Segmentationcited by 32
  7. 2024Centerline Boundary Dice Loss for Vascular Segmentationcited by 27
  8. 2025MedSAM3: Delving into Segment Anything with Medical Conceptscited by 25
  9. 2025MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal outputcited by 14
  10. 2025MedSG-Bench: A Benchmark for Medical Image Sequences Groundingcited by 8
  11. 2025Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumescited by 4
  12. 2023MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomographycited by 2
14 more
  1. 2024Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?cited by 1
  2. 2025COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentationcited by 1
  3. 2025TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracycited by 1
  4. 2025RefTr: Recurrent Refinement of Confluent Trajectories for 3D Vascular Tree Centerlinescited by 1
  5. 2026VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generationcited by 1
  6. 2026Sparse Representation Learning for Vesselscited by 1
  7. 2024Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentationcited by 0
  8. 2024SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentationcited by 0
  9. 2025Self-adaptive vision-language model for 3D segmentation of pulmonary artery and veincited by 0
  10. 2025Learning from Disagreement: A Group Decision Simulation Framework for Robust Medical Image Segmentationcited by 0
  11. 2026Learning Topology-Aware Implicit Field for Unified Pulmonary Tree Modeling with Incomplete Topological Supervisioncited by 0
  12. 2026Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglementcited by 0
  13. 2026SBP-Net: Learning Thin Structure Reconstruction with Sliding-Box Projectionscited by 0
  14. 2026Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Needcited by 0