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Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks.

Junyu Chen1,2, Ye Li1,2, Licia P Luna2

  • 1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.

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This study introduces a novel convolutional neural network (ConvNet) segmentation method for quantitative bone single-photon emission computed tomography (QBSPECT) images. The developed technique offers fast and accurate lesion and bone segmentation, improving bone metastasis assessment.

Keywords:
convolutional neural networksfuzzy C-meansimage segmentationnuclear medicine

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Biology

Background:

  • Quantitative bone single-photon emission computed tomography (QBSPECT) offers superior bone metastasis assessment compared to planar scintigraphy.
  • Accurate segmentation of lesions and bone in QBSPECT images is crucial for evaluating treatment response.
  • Manual segmentation of QBSPECT images is time-consuming and relies on expert interpretation.

Purpose of the Study:

  • To develop a fast and robust automated segmentation method for QBSPECT images.
  • To partition QBSPECT images into lesion, bone, and background regions.
  • To improve the quantitative assessment of bone metastasis through enhanced image segmentation.

Main Methods:

  • A convolutional neural network (ConvNet) was trained using novel unsupervised, semi-supervised, and supervised loss functions.
  • Loss functions were derived from the Fuzzy C-means (FCM) algorithm.
  • The method was evaluated against conventional clustering and other ConvNet loss functions using simulated and clinical QBSPECT/CT data.

Main Results:

  • The proposed unsupervised method outperformed conventional clustering in accuracy and reduced computation time by 200-fold on simulated data.
  • The semi-supervised ConvNet achieved high Dice Similarity Coefficients (DSCs) for lesion and bone segmentation on clinical QBSPECT/CT images.
  • The proposed method demonstrated statistically significant improvements over standard segmentation loss functions.

Conclusions:

  • A novel ConvNet-based segmentation method with adaptable loss functions (unsupervised, semi-supervised, supervised) was successfully developed.
  • The method provides rapid and reliable segmentation of lesions and bone in QBSPECT/CT imaging.
  • This approach holds potential for broader applications in medical image segmentation.