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Related Experiment Video

Updated: May 24, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Artificial intelligence based semi-automatic segmentation for orbital tumor preoperative modeling.

Margaret B Mitchell1,2, Ryan Bartholomew1,2, Angela Zhu3

  • 1Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, MA, USA.

Orbit (Amsterdam, Netherlands)
|February 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a 3D reconstruction method using 3DSlicer to assess benign orbital tumor resectability for endoscopic surgery. The models aid in determining surgical candidacy and approach for primary benign orbital tumors (PBOTs).

Keywords:
Orbital tumorsPBOTartificial intelligenceendoscopic orbital surgeryimaging segmentation

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Area of Science:

  • Neurosurgery
  • Ophthalmology
  • Medical Imaging

Background:

  • Endoscopic surgery is increasingly used for primary benign orbital tumor (PBOT) removal.
  • Preoperative imaging interpretation is crucial for determining patient suitability for endoscopic resection.
  • Otolaryngologists and ophthalmologists face challenges in assessing tumor resectability using current imaging methods.

Purpose of the Study:

  • To develop a semi-automatic 3D reconstruction technique for PBOTs.
  • To enhance the assessment of tumor resectability for endoscopic surgery.
  • To aid surgeons in selecting appropriate surgical approaches based on detailed tumor visualization.

Main Methods:

  • Utilized 3D Slicer software for semi-automatic segmentation of tumors and critical anatomical structures from preoperative imaging.
  • Created patient-specific 3D models for tumors classified under the ORBIT (Orbital Resection by Intranasal Technique) system.
  • Generated models representing all five ORBIT classes to highlight differentiating features.

Main Results:

  • Developed 3D reconstructions for all five ORBIT classes of PBOTs.
  • Models visually distinguished key features such as intraconal vs. extraconal location.
  • Demonstrated tumor proximity to the ophthalmic artery/optic nerve intersection and potential extension into pterygopalatine/infratemporal fossae.

Conclusions:

  • Semi-automatic 3D reconstructions using 3DSlicer offer valuable insights into PBOT characteristics.
  • These models provide quantitative and qualitative data to guide decisions on tumor resectability.
  • The technique supports the selection of optimal surgical strategies for endoscopic orbital tumor removal.