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DECIDE: A decoupled semantic and boundary learning network for precise osteosarcoma segmentation by integrating

Yinhao Wu¹, Jianqi Li², Xinxin Wang¹

¹Department of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 518107, China.

Computers in Biology and Medicine

|April 6, 2024

Summary

Precise automated osteosarcoma segmentation (AOSMM) is improved by the novel DECIDE network. It effectively fuses multi-modality MRI data and captures complex tumor features for better treatment planning.

Keywords:

Attention mechanisms Context aggregation Multi-modality MRI Multi-task learning Osteosarcoma segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Automated Osteosarcoma Segmentation (AOSMM) is crucial for tumor evaluation and treatment planning.
Current methods struggle with multi-modality MRI diversity, tumor heterogeneity, and boundary ambiguity.
Existing approaches often neglect complementary information from multiple MRI modalities and fail to model long-range tumor feature dependencies.

Purpose of the Study:

To develop a precise automated osteosarcoma segmentation method using multi-modality MRI.
To address limitations in feature representation and capture complex tumor characteristics.
To improve the accuracy and stability of osteosarcoma segmentation.

Main Methods:

Proposed a Decoupled Semantic and Boundary Learning Network (DECIDE).
Introduced a Multi-modality Feature Fusion and Recalibration (MFR) module for adaptive feature fusion using channel-wise dependencies.
Incorporated a Lesion Attention Enhancement (LAE) module for capturing global contextual dependencies and a Boundary Context Aggregation (BCA) module for enhancing semantic representations with boundary information.

Main Results:

DECIDE demonstrated exceptional performance in osteosarcoma segmentation.
The method surpassed state-of-the-art techniques in accuracy and stability.
Experiments confirmed the effectiveness of the MFR, LAE, and BCA modules in improving segmentation precision.

Conclusions:

The proposed DECIDE network offers a significant advancement in automated osteosarcoma segmentation.
Integrating multi-modality MRI information and advanced attention mechanisms enhances tumor characterization and segmentation accuracy.
DECIDE provides a robust and effective solution for clinical applications in osteosarcoma management.