Light scattering is a powerful tool that has been widely applied in various scenarios, such as nanoparticle analysis, single-cell measurement, and blood flow monitoring. However, noise is always a concerning and challenging issue in light scattering imaging (LSI) due to the complexity of noise sources.

Light scattering is a fundamental optical phenomenon used to gather information about biological tissues without requiring labels or contrast agents. In neurosurgery, its importance has grown significantly due to its ability to provide real-time, high-resolution, and non-invasive imaging.

• Label-free imaging: Light scattering allows visualization of brain structures and tumor margins without contrast dyes, reducing risk and complexity.
• Real-time guidance: Intraoperative Light Scattering Imaging (LSI) can assist in navigating delicate brain areas, improving the precision of tumor resections.

• Contrast enhancement: Tumors and healthy tissue scatter light differently. LSI enhances the contrast-to-noise ratio (CNR), making it easier to detect tumor boundaries.
• Avoidance of residual tumor: Accurate margin identification reduces the risk of incomplete resections and tumor recurrence.

• Detection of ischemia or necrosis: Changes in scattering patterns can indicate tissue viability, helping assess the impact of surgical manipulations.
• Functional mapping: LSI can potentially be combined with perfusion or blood flow imaging to assess functional integrity during surgery.

• Endoscopic integration: Light scattering technologies can be integrated into neuroendoscopes, allowing enhanced visualization in ventricular and skull-base surgeries.
• Reduced surgical trauma: Better imaging leads to smaller craniotomies and more targeted interventions.

• Nanoparticle tracking: LSI enables real-time monitoring of functionalized nanoparticles used in diagnostic and therapeutic applications.
• Brain tissue characterization: It provides detailed information on microstructural changes, useful in tumor grading and understanding disease progression.

• Denoising and enhancement: Deep learning models can process LSI data to reduce noise, improving signal-to-noise ratio (SNR) and clinical utility.
• Automated decision support: Combined with AI, LSI can contribute to automated tissue classification or surgical guidance systems.

Light scattering offers neurosurgeons a powerful, non-invasive window into the brain, improving surgical precision, safety, and diagnostic capabilities. As imaging hardware and AI-based processing evolve, its role in next-generation neurosurgical workflows is set to expand.

In a work of Lin et al., a deep learning-based adaptive denoising framework has been established to explore the temporal information on LSI videos, aiming to provide an unsupervised and self-learning denoising strategy for various application scenarios of LSI. This novel framework consists of three stages: noise distribution maps for describing the characteristics of LSI noise, video denoising based on the unsupervised learning of the FastDVDNet network, and denoising effect discrimination to screen the best denoised result for further processing. The denoising performance is validated by two common LSI applications: nanoparticle analysis and label-free identification of single cells. The result shows that the method compares favorably to existing methods in suppressing the background noise and enhancing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of LSI. Consequently, the successful analysis of both particle size distribution and cell classification can be notably improved. The proposed unsupervised adaptive denoising method is expected to offer a powerful tool toward a fully automated denoising and improved accuracy in extensive applications of LSI ¹⁾.

Lin et al. address a critical bottleneck in Light Scattering Imaging (LSI)—the pervasive problem of complex and variable noise, which limits the sensitivity and reliability of LSI in real-world biomedical applications. The use of a deep learning-based unsupervised denoising framework is well-motivated, given the success of such models in other imaging modalities. The study’s focus on adaptive, label-free denoising is particularly significant for fields like nanoparticle tracking and single-cell analysis, where ground truth data is often unavailable.

2. Methodological Strengths Three-Stage Pipeline: The proposed framework is thoughtfully structured. It first characterizes noise through distribution maps, then denoises using an unsupervised variant of FastDVDNet, and finally incorporates a discrimination stage to select the optimal denoised output. This layered approach is methodologically sound and balances flexibility with robustness.

Unsupervised Learning: The use of self-supervised learning eliminates the need for annotated datasets, a major advantage in biomedical imaging where labeling is impractical or infeasible.

Validation Across Applications: The validation in two distinct LSI use cases—nanoparticle analysis and label-free single-cell classification—demonstrates good generalizability of the method.

3. Limitations and Considerations Limited Dataset Scope: While the method is applied to two LSI use cases, the generalizability beyond these domains (e.g., blood flow imaging or in vivo diagnostics) remains speculative until broader datasets are tested.

Denoising Effect Selection: The “discrimination” step, meant to choose the best-denoised output, is not clearly described in terms of criteria or automation. Is this selection based on a learned metric, traditional image quality indices (like PSNR/SSIM), or human inspection?

No Comparison with Supervised DL Models: While unsupervised approaches are advantageous, a comparison with state-of-the-art supervised models (e.g., Noise2Noise, U-Net variants) would help quantify the trade-offs in performance.

Black Box Problem: The authors do not address interpretability or explainability—important in clinical translation where decisions based on denoised images may affect diagnosis or treatment.

4. Contribution to the Field The paper makes a notable contribution by introducing an adaptive, unsupervised, and generalizable approach to video denoising in LSI. It potentially reduces dependency on task-specific models or handcrafted pre-processing pipelines. This could significantly streamline data processing in LSI-based research and diagnostics.

5. Future Directions Expand Application Scenarios: Testing the model on additional datasets (e.g., in vivo blood flow, retinal imaging, or immune cell migration) would further establish its robustness.

Integration with Real-Time Systems: The feasibility of real-time application should be explored, given that LSI is often used in live monitoring scenarios.

Explainable AI Integration: Incorporating explainability modules could increase clinical trust and help with regulatory acceptance.

Conclusion Lin et al.'s study introduces a promising unsupervised deep learning pipeline for denoising LSI videos, showing strong performance in improving SNR and CNR in challenging settings. While the framework is well-structured and addresses a real problem, further clarity on the evaluation strategy, comparisons with other methods, and expanded testing would strengthen the case for its widespread adoption.