Spatiotemporal absorption fluctuation imaging based on U-Net.

SIGNIFICANCE: Full-field optical angiography is critical for vascular disease research and clinical diagnosis. Existing methods struggle to improve the temporal and spatial resolutions simultaneously. AIM: Spatiotemporal absorption fluctuation imaging (ST-AFI) is proposed to achieve dynamic blood flow imaging with high spatial and temporal resolutions. APPROACH: ST-AFI is a dynamic optical angiography based on a low-coherence imaging system and U-Net. The system was used to acquire a series of dynamic red blood cell (RBC) signals and static background tissue signals, and U-Net is used to predict optical absorption properties and spatiotemporal fluctuation information. U-Net was generally used in two-dimensional blood flow segmentation as an image processing algorithm for biomedical imaging. In the proposed approach, the network simultaneously analyzes the spatial absorption coefficient differences and the temporal dynamic absorption fluctuation. RESULTS: The spatial resolution of ST-AFI is up to 4.33 µm, and the temporal resolution is up to 0.032 s. In vivo experiments on 2.5-day-old chicken embryos were conducted. The results demonstrate that intermittent RBCs flow in capillaries can be resolved, and the blood vessels without blood flow can be suppressed. CONCLUSIONS: Using ST-AFI to achieve convolutional neural network (CNN)-based dynamic angiography is a novel approach that may be useful for several clinical applications. Owing to their strong feature extraction ability, CNNs exhibit the potential to be expanded to other blood flow imaging methods for the prediction of the spatiotemporal optical properties with improved temporal and spatial resolutions.

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection.

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

Detection of nasopharyngeal cancer using confocal Raman spectroscopy and genetic algorithm technique.

Raman spectroscopy (RS) and a genetic algorithm (GA) were applied to distinguish nasopharyngeal cancer (NPC) from normal nasopharyngeal tissue. A total of 225 Raman spectra are acquired from 120 tissue sites of 63 nasopharyngeal patients, 56 Raman spectra from normal tissue and 169 Raman spectra from NPC tissue. The GA integrated with linear discriminant analysis (LDA) is developed to differentiate NPC and normal tissue according to spectral variables in the selected regions of 792-805, 867-880, 996-1009, 1086-1099, 1288-1304, 1663-1670, and 1742-1752 cm-1 related to proteins, nucleic acids and lipids of tissue. The GA-LDA algorithms with the leave-one-out cross-validation method provide a sensitivity of 69.2% and specificity of 100%. The results are better than that of principal component analysis which is applied to the same Raman dataset of nasopharyngeal tissue with a sensitivity of 63.3% and specificity of 94.6%. This demonstrates that Raman spectroscopy associated with GA-LDA diagnostic algorithm has enormous potential to detect and diagnose nasopharyngeal cancer.