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Validation of Segmented Brain Tumor from MRI Images Using 3D Printingthe.

Ujwal Ashok Nayak1, Mamatha Balachandra1, Manjunath K N1

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.

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Summary

This study introduces a user-guided brain tumor segmentation method using MRI images and 3D printing for accurate tumor quantification. The technique validates segmentation accuracy by comparing measurements from 3D models and physical prints.

Keywords:
3D printingImage SegmentationMedical Image Analysisimage processing

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Radiology

Background:

  • Early brain tumor diagnosis is crucial for effective treatment.
  • Manual segmentation of tumors from volumetric data is time-consuming and challenging.
  • Accurate tumor visualization aids treatment planning.

Purpose of the Study:

  • To develop a user-guided brain tumor segmentation method using Magnetic Resonance Imaging (MRI).
  • To validate the accuracy of the segmentation technique through 3D printing and physical measurement.
  • To improve tumor quantification and radiotherapy delineation.

Main Methods:

  • User-guided segmentation of brain tumors from MRI using the Medical Imaging Interaction Toolkit (MITK).
  • 3D printing of the segmented tumor volume for physical object creation.
  • Measurement comparison between 3D digital model and 3D printed object using electronic calipers.

Main Results:

  • The developed technique achieved consistent measurements between digital and 3D printed tumor models.
  • Statistical analysis (paired t-test) confirmed the accuracy of the segmentation.
  • Observer opinion corroborated the high accuracy of the segmentation method.

Conclusions:

  • The 3D printing-based measurement validation confirms the accuracy of the user-guided segmentation technique.
  • This method offers a reliable approach for precise tumor volume delineation in radiotherapy.
  • Accurate segmentation and quantification are vital for optimizing cancer treatment strategies.