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MAS-Net:Multi-modal Assistant Segmentation Network For Lumbar Intervertebral Disc.

Du Qinhong¹, He Yue¹, Bu Wendong¹

¹Department of Computer Science and Technology, Qingdao University, QingDao, People's Republic of China.

Physics in Medicine and Biology

|August 11, 2023

Summary

A new AI model, MAS-Net, accurately segments lumbar intervertebral discs (LIDs) using multi-modal MRI scans. This automates diagnosis, improving accuracy and efficiency in identifying lumbar disc diseases.

Keywords:

artificial intelligence diagnosis convolution neural networks lumbar intervertebral disc medical image segmentation multi-modal image

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Lumbar disc disease diagnosis relies heavily on expert interpretation of medical images.
Current methods for lumbar intervertebral disc (LID) segmentation are time-consuming and subjective.
Accurate LID segmentation is crucial for intelligent diagnosis but remains challenging due to anatomical complexity.

Purpose of the Study:

To develop an automated method for accurate segmentation and localization of lumbar intervertebral discs (LIDs).
To leverage multi-modal lumbar magnetic resonance images (MRIs) for improved LID segmentation.
To enhance the efficiency and objectivity of lumbar disc disease diagnosis.

Main Methods:

Proposed a novel multi-modal assistant segmentation network (MAS-Net).
MAS-Net incorporates a multi-branch fusion encoder (MBFE), cross-modality correlation evaluation (CMCE), channel fusion transformer (CFT), and a selective Kernel (SK) based decoder.
Utilized skip connections and global pooling for feature enhancement and selective channel weighting.

Main Results:

MAS-Net achieved high performance with a Dice coefficient of 93.08% on the IVD3Seg dataset and 93.22% on the DualModalDisc dataset.
The model outperformed existing state-of-the-art networks in LID segmentation.
Generated accurate 3D models for precise visualization of LIDs.

Conclusions:

MAS-Net offers an automated solution for LID segmentation, addressing diagnostic challenges.
Multi-modal MRI integration enhances information complementation and segmentation accuracy.
The developed network simplifies and clarifies visual representation, aiding medical professionals in diagnosing lumbar disc diseases.