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Automated and robust organ segmentation for 3D-based internal dose calculation.

Mahmood Nazari1,2, Luis David Jiménez-Franco3, Michael Schroeder4

  • 1Technische Universität Dresden, Dresden, TU, Germany. mahmood.nazari@mailbox.tu-dresden.de.

EJNMMI Research
|June 8, 2021
PubMed
Summary

This study developed a fast and accurate deep learning method for segmenting organs in CT scans, improving volumetric dosimetry for kidney and liver treatments in molecular radiotherapy. Automated segmentation accelerates calculations but still requires expert oversight for optimal results.

Keywords:
177LuAutomationCT segmentationDeep learningInternal dosimetryMolecular radiotherapy (MRT)SPECT

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Deep Learning

Background:

  • Accurate organ segmentation is crucial for precise dosimetry in molecular radiotherapy.
  • Current manual segmentation methods are time-consuming and prone to inter-observer variability.

Purpose of the Study:

  • To develop and optimize a deep learning-based computed tomography (CT)-organ segmentation method for kidneys and livers.
  • To validate the method for daily dosimetry applications.
  • To compare dosimetry results from automated versus manual segmentation.

Main Methods:

  • Adapted a convolutional neural network (CNN) for CT image-based organ delineation.
  • Utilized segmented organs as binary masks for volumetric/3D dosimetry calculations.
  • Performed dosimetry calculations for kidneys as source organs.

Main Results:

  • Achieved Dice coefficients of [Formula: see text] for liver and [Formula: see text] for kidney segmentation.
  • Demonstrated computational efficiency suitable for clinical daily routines.
  • Kidney self-absorbed doses calculated via automated segmentation showed a difference of [Formula: see text] compared to manual segmentation in 8 patients.

Conclusions:

  • The proposed automated segmentation approach accelerates volumetric dosimetry for kidney treatments in molecular radiotherapy.
  • While accurate, the automated method necessitates expert supervision due to potential co-registration misalignments between SPECT and CT images.