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Updated: Aug 20, 2025

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

Post-operative glioblastoma multiforme segmentation with uncertainty estimation.

Michal Holtzman Gazit¹, Rachel Faran¹, Kirill Stepovoy¹

¹Novocure, Haifa, Israel.

Frontiers in Human Neuroscience

|November 21, 2022

Summary

Accurate segmentation of post-operative glioblastoma (GBM) is crucial for Tumor Treating Fields (TTFields) planning. This study introduces a novel method using ensemble networks and uncertainty estimation for improved GBM segmentation in post-operative MRI scans.

Keywords:

MRI Tumor Treating Fields glioblastoma multiform segmentation treatment planning

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Last Updated: Aug 20, 2025

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Accurate segmentation of post-operative glioblastoma multiforme (GBM) is critical for effective Tumor Treating Fields (TTFields) treatment planning.
Existing pre-operative segmentation methods show limitations when applied to post-operative GBM MRI scans.

Purpose of the Study:

To develop and validate an automated method for segmenting post-operative GBM in MRI scans.
To improve the accuracy and efficiency of GBM segmentation for TTFields treatment planning.

Main Methods:

An ensemble of segmentation networks combined with Kullback-Leibler divergence for uncertainty estimation.
Integration of surgery type and non-tumorous tissue delineation for automated tumor segmentation.
Validation using a dataset of 340 enhanced T1 MRI scans and a physician review process.

Main Results:

Achieved average Dice scores of 0.81 (whole-tumor), 0.71 (resection), 0.64 (necrotic-core), and 0.68 (enhancing-tissue).
Physicians deemed 72% of segmentations acceptable for treatment planning, with an additional 22% requiring minimal manual editing.
The developed tool facilitates visualization and rapid editing of segmentation results.

Conclusions:

The proposed method enhances post-operative GBM segmentation accuracy and robustness.
The integration of uncertainty visualization and editing tools supports clinical viability.
This approach can streamline TTFields treatment planning by shortening the segmentation process.