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Updated: Jun 11, 2025

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

UNet-based multi-organ segmentation in photon counting CT using virtual monoenergetic images.

Sumin Baek¹, Dong Hye Ye², Okkyun Lee¹

¹Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea.

Medical Physics

|October 7, 2024

Summary

Photon counting detector CT (PCCT) multi-organ segmentation is improved using virtual monoenergetic images (VMIs). This method enhances training stability and segmentation accuracy, especially with fewer energy bins, aiding clinical diagnosis.

Keywords:

deep learning material decomposition multi‐organ segmentation photon counting CT

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Medical Imaging
Radiology
Artificial Intelligence in Medicine

Background:

Multi-organ segmentation is crucial for disease diagnosis, treatment planning, and radiotherapy.
Photon counting detector-based CT (PCCT) offers spectral information that can potentially enhance segmentation performance.

Purpose of the Study:

To propose and evaluate a UNet-based multi-organ segmentation method for PCCT utilizing virtual monoenergetic images (VMIs).
To effectively leverage spectral information from PCCT for improved segmentation accuracy.

Main Methods:

A multi-step process involving noise reduction, material decomposition, VMI generation, and deep learning-based segmentation (3D UNet, Swin UNETR).
VMIs were synthesized across various x-ray energies using basis images.
Segmentation performance was evaluated using Dice Similarity Coefficients (DSC) and 3D visualization on abdominal phantoms.

Main Results:

The proposed VMI-based method demonstrated improved training stability compared to conventional bin-wise image segmentation.
Average DSC for liver, pancreas, and spleen segmentation slightly increased from 0.933 to 0.95, with a reduced standard deviation from 0.066 to 0.047.
Improvements were noted particularly in low-dose scenarios and with fewer energy bins.

Conclusions:

Virtual monoenergetic images (VMIs) enhance training stability for multi-organ segmentation in PCCT.
The VMI approach is particularly beneficial when working with a limited number of energy bins, offering a more robust segmentation solution.