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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive

Donghoon Lee¹, Sadegh Alam¹, Jue Jiang¹

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Medical Physics

|October 28, 2022

Summary

This summary is machine-generated.

Seq2Morph accurately registers longitudinal images for adaptive radiotherapy (ART) by analyzing anatomical changes over time. This novel deep learning method offers fast and precise deformable image registration (DIR), improving ART workflows.

Keywords:

adaptive radiotherapy deep learning deformable image lung cancer registration

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Area of Science:

Medical Imaging
Radiotherapy
Artificial Intelligence

Background:

Adaptive radiotherapy (ART) requires accurate analysis of patient anatomy changes during treatment.
Longitudinal image registration is crucial for monitoring treatment response and enabling ART.

Purpose of the Study:

To develop a novel deep learning-based deformable image registration (DIR) network, Seq2Morph, for simultaneous registration of longitudinal images in radiotherapy.
To analyze patient anatomy changes for improved adaptive radiotherapy (ART).

Main Methods:

Seq2Morph, a novel deep learning DIR network, was designed based on VoxelMorph, incorporating temporal pattern analysis using 3D convolutional long short-term memory.
Bidirectional pathways were added to minimize inverse consistency errors (ICEs).
The network was trained and validated on longitudinal image sets from 50 patients, using a loss function combining intensity similarity, contour similarity, ICE, and deformation regularization.

Main Results:

Seq2Morph demonstrated accurate registration, outperforming VoxelMorph in GTV DICE and 50% HD metrics.
Performance was comparable to LDDMM, but Seq2Morph offered significantly faster inference times (22s vs. ~30 min).
Visualization revealed distinct spatiotemporal anatomy change patterns.

Conclusions:

Seq2Morph provides accurate and fast DIR for longitudinal image studies by exploiting spatial-temporal patterns.
The method aligns with clinical workflows and has potential for both online and offline adaptive radiotherapy (ART).