Work

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

Year: 2025

Type: article

Abstract: Conventional brain tumour progression tracking methods inherently assume consistent image acquisition conditions between baseline and follow-up scans. In practice, however, MRI data often suffer from harmonisation issues due to variations in acquisition protocols, scanner settings, or patient positioning. To address this, we propose an image-based approach capable of identifying and localising changes between baseline and follow-up MRI scans without requiring independent registration or extensive pre-processing. Our registration-invariant method leverages the internal feature maps of a Siamese network composed of two segmentation models based on dilated convolutions, allowing it to learn long-context spatial features. These features are used to detect and localise changes directly from the input images, which are then enhanced using panchromatic sharpening to emphasise both high-level structural and pixel-level differences within the resulting change map. When tested on previously unseen, unregistered MRI scans, the method outperformed baseline models in identifying and localising resected tumour tissue ( $$\varvec{F}_{\varvec{1}} \varvec{= 0.64}$$ and $$\varvec{IoU = 0.57}$$ versus baseline $$\varvec{F}_{\varvec{1}} \varvec{= 0.11}$$ and $$\varvec{IoU = 0.07}$$ ). This performance remained robust under conditions of augmentation, noise, and misalignment ( $$\varvec{F}_{\varvec{1}} \varvec{= 0.55}$$ and $$\varvec{IoU = 0.45}$$ ), highlighting its resilience to real-world imaging variability. Overall, the proposed approach demonstrates strong robustness to spatial misalignment and offers a promising solution for tracking tumour progression and regression in clinical environments where reliable registration cannot be guaranteed.

Source: Journal of Imaging Informatics in Medicine

DOI: 10.1007/s10278-025-01744-8

Authors: Adil Jahouh, Andrei Jalba, Maxime Chamberland

Institution: Eindhoven University of Technology

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,690