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Skeleton-guided 3D convolutional neural network for tubular structure segmentation.

Ruiyun Zhu1, Masahiro Oda2,3, Yuichiro Hayashi2

  • 1Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan. rzhu@mori.m.is.nagoya-u.ac.jp.

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Summary

This study introduces a novel 3D deep learning network that uses skeleton information to improve the segmentation of complex tubular structures in medical imaging. The skeleton-guided network demonstrates superior accuracy in segmenting airways and abdominal arteries from CT scans.

Keywords:
3D convolutional networkCT imageTubular structure segmentation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate segmentation of tubular structures is vital for clinical applications but is hindered by their complexity and data imbalance.
  • Existing methods struggle with the intricate branching and varying sizes of tubular structures in 3D medical images.

Purpose of the Study:

  • To develop a 3D deep learning network incorporating skeleton information for enhanced segmentation of tubular structures.
  • To improve the accuracy and robustness of segmenting challenging anatomical features in medical imaging.

Main Methods:

  • A 3D convolutional neural network was utilized for feature extraction from volumetric CT images.
  • A novel skeleton-guided module was integrated to preserve and leverage skeleton information during segmentation.
  • A sigmoid-adaptive Tversky loss function was proposed for effective training, specifically tailored for skeleton segmentation.

Main Results:

  • Experiments were conducted on chest and abdominal CT datasets, involving 90 and 35 cases, respectively.
  • The proposed method significantly outperformed previous segmentation approaches on both datasets.
  • Achieved high performance metrics, including 93.0% tree length rate for airways and 97.7% precision for abdominal arteries.

Conclusions:

  • A skeleton-guided 3D convolutional network was successfully developed for segmenting tubular structures in 3D medical images.
  • The proposed network effectively segments small and complex tubular structures, surpassing the performance of existing methods.