Does Adding a Modality Really Make Positive Impacts in Incomplete Multi-Modal Brain Tumor Segmentation?

Area of Science:

• Medical image analysis
• Artificial intelligence in medicine
• Computational neuroscience

Background:

• Incomplete multi-modal brain tumor segmentation methods often suffer from performance degradation due to inconsistent modality patterns and quality.
• Direct fusion of diverse modalities can introduce misinformation, negatively impacting segmentation accuracy.
• Existing approaches overlook the positive and negative impacts of individual modalities on the fusion process.

Purpose of the Study:

• To propose a novel pipeline, Positive-Negative impact region Double Calibration (PNDC), to mitigate misinformation transfer in multi-modal brain tumor segmentation.
• To enhance the learning of individual modalities and the fusion process by addressing modality-specific impacts.
• To provide a plug-and-play solution that improves existing incomplete multi-modal segmentation networks without architectural changes or additional parameters.

Main Methods:

• The PNDC pipeline comprises two sub-pipelines: Reverse Audit to identify negative impact regions within each modality, and Forward Checksum to assess fusion reliability in these regions using positive impact regions.
• Negative impact regions and unreliable fusion predictions are utilized to refine individual modality learning and the overall fusion process.
• PNDC is designed as a standard training strategy, compatible with existing networks and requiring no new learnable parameters.

Main Results:

• Extensive experiments demonstrate that PNDC effectively alleviates performance degradation commonly observed in state-of-the-art incomplete multi-modal brain tumor segmentation methods.
• The proposed method successfully addresses the issue of overlooking positive/negative impact regions during modality fusion.
• PNDC shows significant improvements by mitigating misinformation transfer, leading to more robust segmentation outcomes.

Conclusions:

• The PNDC pipeline offers a significant advancement in incomplete multi-modal brain tumor segmentation by effectively handling modality-specific impacts.
• This approach enhances the reliability and accuracy of segmentation by intelligently calibrating modality fusion.
• PNDC provides a universally applicable and parameter-free solution for improving existing segmentation frameworks.