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Clavicle segmentation in chest radiographs.

Laurens Hogeweg1, Clara I Sánchez, Pim A de Jong

  • 1Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, Geert Grooteplein Zuid 18, 6525 GA Nijmegen, The Netherlands. l.hogeweg@rad.umcn.nl

Medical Image Analysis
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Summary
This summary is machine-generated.

This study presents an automated method for segmenting clavicles in chest X-rays, achieving high accuracy. The technique combines pixel classification, active shape models, and dynamic programming for precise anatomical delineation.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Automated delineation of anatomical structures in chest radiographs is challenging due to overlapping structures.
  • Accurate segmentation of the clavicle is crucial for diagnosing various thoracic conditions.

Purpose of the Study:

  • To develop and evaluate an automated technique for segmenting clavicles in posterior-anterior chest radiographs.
  • To improve the accuracy and efficiency of clavicle delineation in medical imaging.

Main Methods:

  • A hybrid approach combining two-stage pixel classification, active shape models, and dynamic programming.
  • Pixel classification for clavicle interior, border, and head, feeding into active shape model segmentation.
  • Dynamic programming with an optimized cost function integrating appearance and shape information.

Main Results:

  • The automated method achieved a mean contour distance of 1.1±1.6mm on 249 test images.
  • An intersection over union score of 0.86±0.10 was obtained, demonstrating robust performance.
  • The method's performance was validated against previous techniques and human observers on a diverse dataset.

Conclusions:

  • The proposed automated technique offers a reliable and accurate solution for clavicle segmentation in chest radiographs.
  • This method has the potential to enhance diagnostic capabilities in thoracic imaging.
  • The publicly available dataset will facilitate further research and development in automated radiological analysis.