The Segmentation of Therapeutic Target Area in Glioma Cancer Patients by Transfer Learning

Document Type : Original Article(s)

Authors

1 MSc Student, Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

2 PhD Student, Department of Medical Physics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

3 Assistant Professor, Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

4 Professor, Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: This study was conducted in order to investigate the power and efficiency of transfer learning in solving the problem of deep learning data volumes for automatic segmentation of the treatment target area in glioma cancer patients.
Methods: In this study, T1, T2 and Flair images of one hundred patients whose glioma cancer was confirmed were used. After quality review, all images were normalized and resized. Then the images were given to a model in two modes with and without transfer learning and their performance was evaluated with the degree of similarity, overlap, sensitivity and accuracy.
Findings: The results of our study show that transfer learning can increase the efficiency of automatic segmentation and increase the similarity of automatic segmentation with manual segmentation to more than 76% in Flair images. Also, this method has increased the speed of reaching the desired result in T2 images that could not improve the results.
Conclusion: Deep learning in automatic segmentation can overcome the limitations caused by data volume in glioma patients and improve their performance.

Keywords

Main Subjects

References

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Journal of Isfahan Medical School
Volume 41, Issue 708
4th Week, April
March and April 2023
Pages 96-101

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History

  • Receive Date: 25 February 2023
  • Accept Date: 03 April 2023

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