Brain tumor grading diagnosis using transfer learning based on optical coherence tomography.

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

Differentiation of primary central nervous system lymphoma from glioblastoma using optical coherence tomography based on attention ResNet.

Significance: Differentiation of primary central nervous system lymphoma from glioblastoma is clinically crucial to minimize the risk of treatments, but current imaging modalities often misclassify glioblastoma and lymphoma. Therefore, there is a need for methods to achieve high differentiation power intraoperatively. Aim: The aim is to develop and corroborate a method of classifying normal brain tissue, glioblastoma, and lymphoma using optical coherence tomography with deep learning algorithm in an ex vivo experimental design. Approach: We collected tumor specimens from ordinal surgical operations and measured them with optical coherence tomography. An attention ResNet deep learning model was utilized to differentiate glioblastoma and lymphoma from normal brain tissues. Results: Our model demonstrated a robust classification power of detecting tumoral tissues from normal tissues and moderate discrimination between lymphoma and glioblastoma. Moreover, our results showed good consistency with the previous histological findings in the pathological manifestation of lymphoma, and this could be important from the aspect of future clinical practice. Conclusion: We proposed and demonstrated a quantitative approach to distinguish different brain tumor types. Using our method, both neoplasms can be identified and classified with high accuracy. Hopefully, the proposed method can finally assist surgeons with decision-making intraoperatively.

Degree of polarization uniformity for dental calculus visualization.

During the treatment for periodontitis, the removal of dental calculus is essential. Previously, we have proposed the DAM algorithm for intuitive identification of the site of lesion, enabling the non-contact assessment during the operation. Nonetheless, the delineation of dental calculus was still imperfect. To this end, here we utilized the power of polarization-sensitive optical coherence tomography and evaluated the contrast called degree of polarization uniformity for dental calculus visualization. The result showed that the selected index demonstrated excellent contrast of dental calculus from other normal dental hard tissues. The proposed contrast is promising for accurate dental calculus delineation.

Neurosurgical brain tumor detection based on intraoperative optical intrinsic signal imaging technique: A case report of glioblastoma.

The delineation of brain tumor margins has been a challenging objective in neurosurgery for decades. Despite the development of various preoperative imaging techniques, the current methodology is still insufficient for clinical practice. We present an intraoperative optical intrinsic signal imaging system for brain tumor surgery and establish a data processing procedure model to localize tumors. From the experimental result of a glioblastoma patient, we observe a relative small oscillation of ΔHbD in tumor region and speculate that vessels in tumor region have poor ability to provide oxygen. We applied the same data processing procedure on the second time data and proclaimed a successful surgery. Figure: Merged ΔHbD image captured prior and posterior to tumor removal.

Optical Polarimetric Detection for Dental Hard Tissue Diseases Characterization.

Dental enamel constitutes the outer layer of a crown of teeth and grows nearly parallel. This unique nanostructure makes enamel possess birefringence properties. Currently, there is still no appropriate clinical solution to examine dental hard tissue diseases. Therefore, we developed an optical polarization imaging system for diagnosing dental calculus, caries, and cracked tooth syndrome. By obtaining Stokes signals reflected from samples, Mueller images were constructed and analyzed using Lu-Chipman decomposition. The results showed that diattenuation and linear retardance images can distinguish abnormal tissues. Our result also aligns with previous studies assessed by other methods. Polarimetric imaging is promising for real-time diagnosing.