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Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning.
Miaoyun Zhao1, Li Wang1, Jiawei Chen1
1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 20, 2018
Summary
This study introduces Deep-supGAN, a novel method for segmenting bony structures from MRI scans. It generates clearer CT images from MRI, improving segmentation accuracy for craniomaxillofacial surgeries.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Computed Tomography (CT) is vital for craniomaxillofacial (CMF) surgery planning but involves radiation risks.
- Magnetic Resonance Imaging (MRI) is safe and excels at soft tissue visualization, but bony structures are poorly depicted.
- Accurate segmentation of bony structures from MRI is challenging due to poor bone contrast.
Purpose of the Study:
- To develop an automated method for segmenting bony structures from MRI, overcoming the limitations of CT.
- To propose a novel cascaded generative adversarial network (Deep-supGAN) for enhanced medical image segmentation.
- To improve the accuracy and detail of bony structure segmentation from MRI for CMF applications.
Main Methods:
- A cascaded generative adversarial network (Deep-supGAN) architecture was developed.
- The first network block generates high-quality CT images from MRI data.
- The second block segments bony structures using both MRI and the synthesized CT image, employing a deep-supervision discriminator for multi-level feature discrimination.
Main Results:
- The proposed Deep-supGAN method successfully generated CT images with enhanced structural details from MRI.
- Bony structures were segmented more accurately compared to existing state-of-the-art methods.
- The deep-supervision discriminator improved segmentation by considering both voxel and perceptual levels.
Conclusions:
- Deep-supGAN offers a promising, radiation-free approach for bony structure segmentation in CMF surgery.
- The method enhances the utility of MRI for surgical planning by enabling detailed visualization of bony anatomy.
- This work advances automatic medical image segmentation, particularly for challenging bony structures in MRI.
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