Conference Paper2023Cited by 2

Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN)

Syed Alif Ul Alam, Saadia Binte Alam, Sourav Saha, Mahmudul Haque, Rashedur Rahman, Syoji Kobashi

2023 26th International Conference on Computer and Information Technology (ICCIT)

IEEE, pp. 1-6

DOI: 10.1109/iccit60459.2023.10441155URL

Abstract

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters.

Keywords

  • pelvic fractures
  • hip fracture
  • X-ray images
  • U-Net
  • DenseNet121
  • InceptionV3
  • automated fracture detection
  • computational efficiency
  • intersection over union

CCDS Authors

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

  • Associate Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Md Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

  • Assistant Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

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  1. 1.Samiul Karim Mazumder, Nafiul Islam Santo, Md. Samiul Alom, Md. Abdul Awal, Ishtiak Al Mamoon. (2025). Advancing Gastrointestinal Tract Segmentation: Dense-UPerNet and a Benchmark of 18 Encoder-Decoder Models. IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 616–621[10.1109/becithcon69222.2025.11504046]
  2. 2.Elina Huerfano, Leonardo Flórez-Valencia. (2025). Segmentation of Pelvic Radiographs After Total Hip Arthroplasty Using U-Net Variants: Toward Morphometric Automation. International Symposium on Medical Information Processing and Analysis (SIPAIM), 1–5[10.1109/sipaim67325.2025.11283334]
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