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Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

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UDRSNet: An unsupervised deformable registration module based on image structure similarity.

Yun Wang¹, Chongfei Huang², Wanru Chang¹

¹School of Mathematical Sciences, Zhejiang University, Hangzhou, China.

Medical Physics

|February 14, 2024

Summary

This study introduces an unsupervised deep learning method for medical image registration, achieving high accuracy without needing ground truth labels. The approach enhances structural consistency for reliable deformable image registration (DIR).

Keywords:

cascaded network deformable registration structural similarity unsupervised learning

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Area of Science:

Medical Imaging
Deep Learning
Computer Vision

Background:

Deep learning has advanced medical image registration, but supervised methods require accurate ground truth labels.
The quality of supervised registration is constrained by the availability and accuracy of displacement field labels.

Purpose of the Study:

To develop a simple, reliable, and completely unsupervised method for medical image registration.
To leverage image structure similarity for accurate alignment without clinical data labels.

Main Methods:

A deep cascade unsupervised deformable registration approach using ResUnet and spatial transformer layers.
Employed L1-norm regularization for the deformation field and structural similarity (SSIM) loss during training.
Enhanced structural consistency between deformed and reference images via SSIM estimation.

Main Results:

Achieved high performance metrics on CT images: Dice score (0.9873), SSIM (0.9559), NCC (0.9950), and SSD error (0.0313).
Outperformed comparative methods on an ultrasound dataset.
Statistical significance confirmed for the method's improvements.

Conclusions:

The proposed unsupervised model demonstrates simplicity and effectiveness for deformable image registration (DIR).
The model exhibits strong generalization capabilities, validated on liver CT and cardiac ultrasound images.