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Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative

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This study introduces a deep learning model for automated glioma segmentation, achieving high accuracy in both pre-operative and post-operative cases. The model

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Neurosurgical oncology

Background:

  • Quantitative volumetric segmentation of gliomas is crucial for diagnosis, treatment planning, and prognosis assessment.
  • Current methods for glioma segmentation face challenges in automation and clinical integration.
  • Deep learning offers a promising avenue for improving the accuracy and efficiency of glioma segmentation.

Purpose of the Study:

  • To develop and validate a deep learning model for automated preoperative and postoperative glioma segmentation.
  • To create a clinical implementation pipeline to accelerate the adoption of automated segmentation tools.
  • To assess the model's performance and processing time for clinical utility.

Main Methods:

  • A deep learning model, autoencoder regularization-cascaded anisotropic, was developed by integrating autoencoder regularization with a cascaded anisotropic convolutional neural network.
  • A dataset of 437 glioma cases was used, with 40 held-out for testing and the remainder for training and validation.
  • Data augmentation, hyperparameter optimization, and an end-to-end pipeline for routing, preprocessing, and user interaction were implemented.

Main Results:

  • The model achieved high median Dice scores: 0.88 for whole-tumor, 0.89 for tumor core/resection cavity, and 0.81 for enhancing tumor subregions.
  • The model demonstrated robust performance across both preoperative and postoperative cases.
  • The total processing time per case was approximately 10 minutes, including all pipeline stages.

Conclusions:

  • The developed deep learning model and clinical pipeline demonstrate the feasibility of rapid and accurate segmentation of both preoperative and postoperative gliomas.
  • The model's ability to segment postoperative gliomas is clinically significant for patient follow-up.
  • An end-to-end approach facilitates the clinical translation of quantitative volumetric glioma measurement tools.