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

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Published on: November 30, 2022

Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection.

Hyung Rae Lee¹, Wounsuk Rhee², Sam Yeol Chang³

¹Department of Orthopedic Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea.

Bioengineering (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

A novel deep learning model accurately segments neural tissue during biportal endoscopic spine surgery (BESS), enhancing safety and efficacy. This AI tool aids surgeons, particularly less experienced ones, in improving patient outcomes in minimally invasive spinal procedures.

Keywords:

computer vision deep learning endoscopic spine surgery image segmentation neural tissue

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Neurosurgery
Artificial Intelligence
Medical Imaging

Background:

Biportal endoscopic spine surgery (BESS) offers minimally invasive benefits but carries risks like dural tears and neural injury.
Accurate intraoperative identification of neural structures is crucial for BESS safety and efficacy.

Purpose of the Study:

To develop and evaluate a deep learning model for automated neural tissue segmentation in BESS.
To enhance surgical safety and improve patient outcomes through improved visualization.

Main Methods:

A U-Net-like convolutional neural network architecture was utilized.
The model was trained and validated on image data from 28 BESS procedures (2307 training, 635 validation images).
Performance was assessed using Dice-Sorensen coefficient, Jaccard index, precision, recall, and processing speed.

Main Results:

The best model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701.
High precision (0.810) and recall (0.839) were observed, with an average precision of 0.890.
The model demonstrated efficient image processing at 43 ms/frame (23.3 fps), enabling real-time application.

Conclusions:

The developed U-Net-based model shows robust performance in neural tissue segmentation for BESS.
This AI tool has the potential to significantly support spine surgeons, especially those with limited experience.
Further development could enhance the clinical applicability and impact of this technology on surgical outcomes.