mlsanityData to 7 Oct 2026

PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement

arXiv 2305.04294 ↗First posted 7 May 2023eess.IV cs.CVcited by 5

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Abstract (arXiv)

The pelvis, the lower part of the trunk, supports and balances the trunk. Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis and computer-assisted diagnosis and treatment of pelvic diseases. Although PXRs have the advantages of low radiation and reduced cost compared to computed tomography (CT) images, their 2D pelvis-tissue superposition of 3D structures confuses clinical decision-making. In this paper, we propose a PELvis Extraction (PELE) module that utilizes 3D prior anatomical knowledge in CT to guide and well isolate the pelvis from PXRs, thereby eliminating the influence of soft tissue. We conduct an extensive evaluation based on two public datasets and one private dataset, totaling 850 PXRs. The experimental results show that the proposed PELE module significantly improves the accuracy of PXRs landmark detection and achieves state-of-the-art performances in several benchmark metrics, thus better serving downstream tasks.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 7 May 2023.

Builds on

13 of 35 references.

References · 13

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
  3. 2020Structured Landmark Detection via Topology-Adapting Deep Graph Learningcited by 50
  4. 2020Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Modelscited by 45
  5. 2021One-Shot Medical Landmark Detectioncited by 31
  6. 2021You Only Learn Once: Universal Anatomical Landmark Detectioncited by 29
  7. 2019An Attention-Guided Deep Regression Model for Landmark Detection in Cephalogramscited by 27
  8. 2018X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgerycited by 22
  9. 2020Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Imagescited by 22
  10. 2022BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planescited by 9
  11. 2018Fast Multiple Landmark Localisation Using a Patch-based Iterative Networkcited by 4
  12. 2019Weakly Supervised Universal Fracture Detection in Pelvic X-rayscited by 4
1 more
  1. 2021Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graphcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2025SimCroP: Radiograph Representation Learning with Similarity-driven Cross-granularity Pre-trainingcited by 3
  2. 2025U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKVcited by 2
  3. 2024HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detectioncited by 1
  4. 2025Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detectioncited by 0
  5. 2026Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registrationcited by 0