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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

MICDIR: Multi-scale inverse-consistent deformable image registration using UNetMSS with self-constructing graph

Soumick Chatterjee¹, Himanshi Bajaj², Istiyak H Siddiquee²

¹Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Germany; Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Germany; Genomics Research Centre, Human Technopole, Milan, Italy.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|July 28, 2023

Summary

This summary is machine-generated.

This study introduces an enhanced deep learning model for medical image registration, improving accuracy in tracking both small and large deformations. The new method significantly outperforms existing techniques like VoxelMorph on brain MRI datasets.

Keywords:

Deep learning Deformable image registration Graph latent Image registration

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Image registration aligns diverse images into a unified coordinate system, crucial for medical imaging analysis.
Current deep learning methods like VoxelMorph excel at fine deformations but struggle with large anatomical changes due to a lack of global dependency encoding.

Purpose of the Study:

To develop an advanced deep learning model for medical image registration capable of handling both small and large deformations accurately.
To improve the robustness and generalization of image registration models by incorporating global anatomical context.

Main Methods:

The study extends the VoxelMorph approach by integrating a multi-scale UNet for resolution-specific supervision.
A self-constructing graph network (SCGNet) is employed as a latent component to capture intricate structural correlations.
Cycle consistency loss is utilized to ensure inverse-consistent deformations.

Main Results:

The proposed method demonstrated significant improvements over ANTs and VoxelMorph in brain MRI registration tasks.
Achieved Dice scores of 0.8013 ± 0.0243 (intramodal) and 0.6211 ± 0.0309 (intermodal), surpassing VoxelMorph's scores.

Conclusions:

The novel approach effectively addresses the limitations of existing methods in handling large deformations.
The integration of multi-scale supervision, graph networks, and cycle consistency enhances registration accuracy and robustness for medical imaging applications.