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Related Experiment Video

Updated: Jun 13, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

CausalX-Net: a causality-guided explainable segmentation network for brain tumors.

P Suman Prakash¹, Patike Kiran Rao², M Jahir Pasha³

¹Department of Computer Science and Engineering-Artificial Intelligence, G Pullaiah College of Engineering and Technology, Kurnool, Andra Pradesh, India.

Frontiers in Medicine

|November 10, 2025

Summary

This summary is machine-generated.

CausalX-Net, a new AI model, improves brain tumor segmentation on MRI scans by using causal inference for better accuracy and interpretability. This approach offers actionable insights for radiologists, enhancing AI-assisted neuroimaging in clinical settings.

Keywords:

CausalX-Net brain tumor segmentation causal effect (CE) maps counterfactual explanations deep learning explainable artificial intelligence (XAI)

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Area of Science:

Artificial Intelligence
Medical Imaging
Neuroscience

Background:

Brain tumors pose a significant health challenge, with limited clinical interpretability in current AI segmentation methods.
Conventional deep learning models struggle with irregular tumor boundaries and complex MRI patterns.

Purpose of the Study:

To introduce CausalX-Net, a causality-guided explainable segmentation network for brain tumor analysis using multi-modal MRI.
To leverage structural causal modeling for identifying causal influences on segmentation outcomes.

Main Methods:

CausalX-Net utilizes structural causal modeling and interventional reasoning.
Employs counterfactual analysis for "what-if" explanations regarding segmentation outcomes.
Evaluated on the BraTS 2021 dataset for performance and interpretability.

Main Results:

Achieved a Dice Similarity Coefficient of 92.5%, outperforming state-of-the-art CNNs by 4.3%.
Demonstrated competitive inference efficiency.
Generated causal attribution maps and sensitivity analyses for enhanced transparency.

Conclusions:

Integrating causal inference into AI segmentation improves accuracy and provides interpretable, decision-supportive explanations.
CausalX-Net offers actionable insights for radiologists, advancing AI-assisted neuroimaging.
Represents a significant step toward transparent and reliable AI in clinical neuroimaging.