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Deep-learning-based automatic segmentation and classification for craniopharyngiomas.

Xiaorong Yan1, Bingquan Lin2, Jun Fu1

  • 1Department of Neurosurgery, First affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.

Frontiers in Oncology
|May 22, 2023
PubMed
Summary

This study introduces an AI method for automatically segmenting brain structures and classifying craniopharyngiomas using MRI scans. The approach accurately identifies tumor types, aiding surgical planning and prognosis.

Keywords:
QST typing systemclassificationcraniopharyngiomasdeep learningsegmentation

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Craniopharyngioma classification and neuronavigation are crucial for surgical planning and prognosis.
  • Accurate preoperative segmentation and classification of craniopharyngiomas remain a significant challenge.

Purpose of the Study:

  • To develop an automated method for segmenting multiple structures in MRI scans.
  • To detect craniopharyngiomas and enable automatic QST classification using deep learning.
  • To design a diagnostic scale for preoperative QST classification.

Main Methods:

  • A deep learning network was trained on sagittal MRI scans for automatic segmentation of tumors and six surrounding tissues.
  • A multi-input deep learning model was designed for preoperative QST classification.
  • A clinical scale was constructed based on image screening.

Main Results:

  • The automatic segmentation model achieved a Dice coefficient of 0.951 for tumors and 0.8668 for all tissues.
  • The automatic classification model and clinical scale achieved accuracies of 0.9098 and 0.8647, respectively.
  • The study included 133 patients with craniopharyngioma, classifying them into Q, S, and T types.

Conclusions:

  • The automated segmentation model accurately segments multiple structures, aiding tumor localization and neuronavigation.
  • The proposed automated classification model and clinical scale offer high accuracy for QST classification.
  • These tools support surgical planning and improve patient prognosis prediction for craniopharyngiomas.