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Related Experiment Video

Updated: Jun 28, 2026

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Published on: May 8, 2018

Investigating Transformer Encoding Techniques to Improve Data-Driven Volume-to-Surface Liver Registration for

Michael Young¹, Zixin Yang¹, Richard Simon²

¹Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA.

Data Engineering in Medical Imaging : First MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. DEMI (Workshop) (1St : 2023 : Vancouver, B.C.)

|August 14, 2024

Summary

This summary is machine-generated.

This study enhances surgical navigation by improving the accuracy of registering pre-operative medical images with intra-operative views during laparoscopic liver procedures. A novel transformer-based network, UTNet, significantly boosts registration accuracy for minimally invasive interventions.

Keywords:

Laparoscopy Machine Learning Neural Network Nonrigid Registration Transformer

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Imaging
Surgical Navigation
Artificial Intelligence in Medicine

Background:

Minimally invasive surgery requires precise instrument navigation, often relying on medical imaging due to limited direct organ visualization.
Intra-operative video in laparoscopic liver surgery offers a restricted view, lacking internal lesion data from pre-procedural scans.
Accurate registration of pre-operative data to the intra-operative setting is crucial for enhanced visualization and navigation.

Purpose of the Study:

To improve the accuracy and robustness of nonrigid volume-to-surface liver registration for laparoscopic interventions.
To adapt state-of-the-art deep learning frameworks, specifically transformer-based networks, for enhanced liver registration.
To evaluate the performance of network architecture modifications in predicting displacement fields for accurate registration.

Main Methods:

Leveraged deep learning frameworks to implement and test various network architecture modifications for liver registration.
Adapted a transformer-based segmentation network for predicting optimal displacement fields in nonrigid registration.
Evaluated performance using metrics such as mean displacement error across diverse datasets.

Main Results:

A transformer-based network architecture, UTNet, demonstrated significant improvements over baseline methods.
The UTNet architecture achieved a mean displacement error on the order of 4 mm.
The proposed approach showed enhanced accuracy and robustness in volume-to-surface liver registration.

Conclusions:

Transformer-based networks, particularly UTNet, show great promise for improving image registration in image-guided surgery.
Accurate registration is vital for overcoming limitations in intra-operative visualization during laparoscopic liver procedures.
This work contributes to advancing AI-driven solutions for more precise and effective minimally invasive surgical interventions.