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Biomedical Engineering

Deep Learning Approaches for Brain Tumor Segmentation

Segmentation models are shaping neuro-oncology workflows and decision support.

Dr. Sarah AhmedDr. Sarah Ahmed
May 10, 2026
9 min read
981 views
Dr. Sarah Ahmed

Dr. Sarah Ahmed

Associate Professor, Department of Biomedical Engineering

University of Toronto, Canada

Dr. Sarah Ahmed's research focuses on medical image analysis, machine learning applications in healthcare, and computer vision.
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Abstract

Practical segmentation pipelines are improving tumor detection and measurement consistency.

1. Introduction

Clinical imaging teams benefit when segmentation tools reduce manual workload and support follow-up comparisons.

2. Clinical Context

Segmentation highlights suspicious regions and can standardize volumetric assessment.
  • Lesion boundary awareness
  • Consistency in follow-up measurement
  • Faster reporting support

3. Model Selection

U-Net variants remain popular because they balance performance and annotation efficiency.

4. Future Directions

Foundation models and better scanner generalization remain promising next steps.

5. Conclusion

Segmentation is becoming an assistive standard in neuro imaging.

References

  1. Reference 1
  2. Reference 2
  3. Reference 3
View on PubMedDOI: 10.1000/example1Download PDF
Tags:Deep LearningTumor SegmentationMRI
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