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DGMSNet: Spine segmentation for MR image by a detection-guided mixed-supervised segmentation network.

Shumao Pang¹, Chunlan Pang², Zhihai Su³

¹School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China.

Medical Image Analysis

|November 18, 2021

Summary

This summary is machine-generated.

This study introduces a novel detection-guided mixed-supervised segmentation network (DGMSNet) for automated spine segmentation in MR images. DGMSNet improves accuracy by combining segmentation and keypoint detection, overcoming limitations of existing methods.

Keywords:

Deep learning Ensemble learning Mixed-supervised segmentation Spine

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Spine segmentation in MR images is crucial for diagnosing and treating spinal diseases.
Existing methods struggle with inter-class similarity and require large annotated datasets.

Purpose of the Study:

To develop a novel detection-guided mixed-supervised segmentation network (DGMSNet) for automated spine segmentation.
To overcome the limitations of existing supervised methods by utilizing both pixel-level and keypoint annotations.

Main Methods:

DGMSNet employs a dual-path architecture: one for segmentation and another for keypoint detection (regression network).
A detection-guided learner generates dynamic parameters for adaptive convolution, enhancing semantic feature maps.
Mixed-supervised learning utilizes a combination of segmentation and detection losses.
A detection-guided label fusion approach integrates predictions from multiple models during inference.

Main Results:

DGMSNet achieved state-of-the-art performance on T2-weighted MR images.
Mean Dice similarity coefficients of 94.39% for vertebral bodies and 87.21% for intervertebral discs were obtained.
The method demonstrated effectiveness on both in-house and public datasets.

Conclusions:

DGMSNet offers a robust and accurate solution for automated spine segmentation.
The proposed mixed-supervised approach effectively reduces reliance on large pixel-level annotated datasets.
This advancement has significant implications for spinal disease diagnosis and treatment planning.