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Updated: Jan 12, 2026

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

Published on: August 26, 2014

Image-Based Identification and Localisation of Changes in Intraoperative Brain Tumour Resection.

Adil Jahouh¹, Andrei Jalba¹, Maxime Chamberland²

¹Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands.

Journal of Imaging Informatics in Medicine

|November 7, 2025

Summary

This summary is machine-generated.

This study introduces a novel image-based method for tracking brain tumor changes on MRI scans, overcoming issues with inconsistent image acquisition. The approach accurately identifies tumor progression and regression without needing image registration, showing high robustness to real-world imaging variability.

Keywords:

Change Intraoperative Resection Segmentation Siamese network Tumour

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Last Updated: Jan 12, 2026

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Published on: August 26, 2014

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Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy

Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy

Published on: May 3, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Conventional methods for tracking brain tumor progression assume consistent MRI scan conditions.
Real-world MRI data often exhibit harmonization issues due to variations in acquisition protocols, scanner settings, or patient positioning.
These inconsistencies complicate accurate tumor progression assessment.

Purpose of the Study:

To develop an image-based approach for identifying and localizing changes between baseline and follow-up MRI scans.
To create a method that does not require independent registration or extensive pre-processing.
To enhance the tracking of tumor progression and regression in clinical settings with variable imaging quality.

Main Methods:

A registration-invariant Siamese network utilizing dilated convolutions to learn long-context spatial features.
Directly detecting and localizing changes from input images using learned feature maps.
Employing panchromatic sharpening to enhance both high-level structural and pixel-level differences in the change map.

Main Results:

The proposed method outperformed baseline models in identifying and localizing resected tumor tissue on unregistered MRI scans (F1=0.64, IoU=0.57 vs. baseline F1=0.11, IoU=0.07).
Performance remained robust under augmentation, noise, and misalignment conditions (F1=0.55, IoU=0.45).
Demonstrated strong robustness to spatial misalignment, a common issue in clinical MRI.

Conclusions:

The developed approach offers a robust solution for tracking brain tumor progression and regression.
It effectively addresses harmonization issues and spatial misalignment in MRI data.
Presents a promising tool for clinical environments where reliable image registration is not always feasible.