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Temporally consistent longitudinal brain tumor segmentation using a temporal spatial transformer network.

Sandeep Kumar Mathivanan¹, Shamala K Subramaniam², Dafik³

¹School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.

Scientific Reports

|May 19, 2026

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This summary is machine-generated.

This study introduces the Temporal-Spatial Transformer Network (TST-Net) for longitudinal brain tumor segmentation. TST-Net enhances accuracy by integrating temporal dynamics and spatial attention in MRI analysis.

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Longitudinal brain tumor segmentation is crucial for monitoring progression and treatment response.
Current deep learning methods often analyze single MRI time points, limiting the incorporation of temporal dynamics.
There is a need for advanced techniques to effectively segment brain tumors over time using MRI data.

Purpose of the Study:

To propose and evaluate the Temporal-Spatial Transformer Network (TST-Net) for longitudinal brain tumor segmentation.
To leverage temporal dynamics and spatial attention for improved segmentation accuracy.
To provide an efficient tool for clinical applications in brain tumor analysis.

Main Methods:

Developed Temporal-Spatial Transformer Network (TST-Net) incorporating temporal and spatial attention modules.

Keywords:

Cancer diagnosis Deep learning Longitudinal MRI Medical imaging Neural networks Temporal attention Transformer networks Tumor segmentation

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Utilized a temporal attention module to integrate progression-aware information from successive MRI images.

Employed a spatial attention module to enhance tumor detection and segmentation.

Preprocessed BraTS longitudinal MRI data and performed end-to-end training on pre-aligned datasets.

Main Results:

TST-Net achieved superior performance compared to existing methods in longitudinal brain tumor segmentation.
Obtained Dice similarity coefficients of 86.0% for improving tumor segmentation, 88.0% for tumor core, and 91.0% for overall tumor segmentation.
Demonstrated that integrating temporal and spatial attention significantly increases segmentation accuracy and reduces inter-scan inconsistencies.

Conclusions:

Incorporating temporal information and spatial attention enhances the accuracy of longitudinal brain tumor segmentation from MRI.
TST-Net effectively addresses inconsistencies between follow-up scans and improves tumor area detection.
TST-Net offers an efficient and accurate solution for clinical brain tumor segmentation, improving monitoring and treatment evaluation.