The Potential of Natural Carotenoids-Containing Sericin of the Domestic Silkworm Bombyx mori.

Sericin derived from the white cocoon of Bombyx mori has been attracting more attention for its utilization in food, cosmetics, and biomedicine. The potential health benefits of natural carotenoids for humans have also been well-established. Some rare strains of Bombyx mori (B. mori) produce yellow-red cocoons, which endow a potential of natural carotenoid-containing sericin. We hypothesized that natural carotenoid-containing sericin from yellow-red cocoons would exhibit better properties compared with white cocoon sericin. To investigate the physicochemical attributes of natural carotenoid-containing sericin, we bred two silkworm strains from one common ancestor, namely XS7 and XS8, which exhibited different cocoon colors as a result of the inconsistent distribution of lutein and ß-carotene. Compared with white cocoon sericin, the interaction between carotenoids and sericin molecules in carotenoid-containing sericin resulted in a unique fluorescence emission at 530, 564 nm. The incorporation of carotenoids enhanced the antibacterial effect, anti-cancer ability, cytocompatibility, and antioxidant of sericin, suggesting potential wide-ranging applications of natural carotenoid-containing sericin as a biomass material. We also found differences in fluorescence characteristics, antimicrobial effects, anti-cancer ability, and antioxidants between XS7 and XS8 sericin. Our work for the first time suggested a better application potential of natural carotenoid-containing sericin as a biomass material than frequently used white cocoon sericin.

VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification.

BACKGROUND: Quantitative plaque assessment based on 3D magnetic resonance (MR) vessel wall imaging (VWI) has been shown to provide valuable numerical markers of the burden and risk of intracranial atherosclerotic disease (ICAD). However, plaque quantification is currently time-consuming and observer-dependent due to the demand for heavy manual effort. A VWI-dedicated automated processing pipeline (VWI-APP) is desirable. PURPOSE: To develop and evaluate a VWI-APP for end-to-end quantitative analysis of intracranial atherosclerotic plaque. METHODS: We retrospectively enrolled 91 subjects with ICAD (80 for pipeline development, 10 for an end-to-end pipeline evaluation, and 1 for demonstrating longitudinal plaque assessment) who had undergone VWI and MR angiography. In an end-to-end evaluation, diameter stenosis (DS), normalized wall index (NWI), remodeling ratio (RR), plaque wall contrast ratio (CR), and total plaque volume (TPV) were quantified at each culprit lesion using the developed VWI-APP and a computer-aided manual approach by a neuroradiologist, respectively. The time consumed in each quantification approach was recorded. Two-sided paired t-tests and intraclass correlation coefficient (ICC) were used to determine the difference and agreement in each plaque metric between VWI-APP and manual quantification approaches. RESULTS: There was no significant difference between VWI-APP and manual quantification in each plaque metric. The ICC was 0.890, 0.813, 0.827, 0.891, and 0.991 for DS, NWI, RR, CR, and TPV, respectively, suggesting good to excellent accuracy of the pipeline method in plaque quantification. Quantitative analysis of each culprit lesion on average took 675.7 s using the manual approach but shortened to 238.3 s with the aid of VWI-APP. CONCLUSIONS: VWI-APP is an accurate and efficient approach to intracranial atherosclerotic plaque quantification. Further clinical assessment of this automated tool is warranted to establish its utility in the risk assessment of ICAD lesions.

Intracranial vessel wall segmentation with deep learning using a novel tiered loss function incorporating class inclusion.

PURPOSE: To develop an automated vessel wall segmentation method on T1-weighted intracranial vessel wall magnetic resonance images, with a focus on modeling the inclusion relation between the inner and outer boundaries of the vessel wall. METHODS: We propose a novel method that estimates the inner and outer vessel wall boundaries simultaneously, using a network with a single output channel resembling the level-set function height. The network is driven by a unique tiered loss that accounts for data fidelity of the lumen and vessel wall classes and a length regularization to encourage boundary smoothness. RESULTS: Implemented with a 2.5D UNet with a ResNet backbone, the proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance (HD) of 0.286 ± 0.436, 0.345 ± 0.419 mm, and mean surface distance (MSD) of 0.083 ± 0.037 and 0.103 ± 0.032 mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a baseline UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477, 0.394 ± 0.431 mm, and MSD 0.087 ± 0.056, 0.119 ± 0.059 mm. Our vessel wall segmentation method achieved substantial improvement in morphological integrity and accuracy compared to benchmark methods. CONCLUSIONS: The proposed method provides a systematic approach to model the inclusion morphology and incorporate it into an optimization infrastructure. It can be applied to any application where inclusion exists among a (sub)set of classes to be segmented. Improved feasibility in result morphology promises better support for clinical quantification and decision.

MRA-free intracranial vessel localization on MR vessel wall images.

Analysis of vessel morphology is important in assessing intracranial atherosclerosis disease (ICAD). Recently, magnetic resonance (MR) vessel wall imaging (VWI) has been introduced to image ICAD and characterize morphology for atherosclerotic lesions. In order to automatically perform quantitative analysis on VWI data, MR angiography (MRA) acquired in the same imaging session is typically used to localize the vessel segments of interest. However, MRA may be unavailable caused by the lack or failure of the sequence in a VWI protocol. This study aims to investigate the feasibility to infer the vessel location directly from VWI. We propose to synergize an atlas-based method to preserve general vessel structure topology with a deep learning network in the motion field domain to correct the residual geometric error. Performance is quantified by examining the agreement between the extracted vessel structures from the pair-acquired and alignment-corrected angiogram, and the estimated output using a cross-validation scheme. Our proposed pipeline yields clinically feasible performance in localizing intracranial vessels, demonstrating the promise of performing vessel morphology analysis using VWI alone.

Ensemble learning and tensor regularization for cone-beam computed tomography-based pelvic organ segmentation.

PURPOSE: Cone-beam computed tomography (CBCT) is a widely accessible low-dose imaging approach compatible with on-table patient anatomy observation for radiotherapy. However, its use in comprehensive anatomy monitoring is hindered by low contrast and low signal-to-noise ratio and a large presence of artifacts, resulting in difficulty in identifying organ and structure boundaries either manually or automatically. In this study, we propose and develop an ensemble deep-learning model to segment post-prostatectomy organs automatically. METHODS: We utilize the ensemble logic in various modules during the segmentation process to alleviate the impact of low image quality of CBCT. Specifically, (1) semantic attention was obtained from an ensemble 2.5D You-only-look-once detector to consistently define regions of interest, (2) multiple view-specific two-stream 2.5D segmentation networks were developed, using auxiliary high-quality CT data to aid CBCT segmentation, and (3) a novel tensor-regularized ensemble scheme was proposed to aggregate the estimates from multiple views and regularize the spatial integrity of the final segmentation. RESULTS: A cross-validation study achieved Dice similarity coefficient and mean surface distance of 0.779 ± $\pm$ 0.069 and 2.895 ± $\pm$ 1.496 mm for the rectum, and 0.915 ± $\pm$ 0.055 and 1.675 ± $\pm$ 1.311 mm for the bladder. CONCLUSIONS: The proposed ensemble scheme manages to enhance the geometric integrity and robustness of the contours derived from CBCT with light network components. The tensor regularization approach generates organ results conforming to anatomy and physiology, without compromising typical quantitative performance in Dice similarity coefficient and mean surface distance, to support further clinical interpretation and decision making.

Technical note: Air bubble-induced performance degradation in automatic rectum segmentation from cone-beam CT.

PURPOSE: Cone-beam computed tomography (CBCT) is widely used for daily anatomy monitoring and can be a potential source to support adaptation. However, low image quality and artifacts limit CBCT's clinical utility. Peristalsis and air bubbles can cause severe artifacts in pelvic CBCT. We have observed that severe air bubble-induced Feldkamp artifacts in the rectum may contribute to low automatic segmentation accuracy. MATERIALS AND METHODS: In this study, air bubbles within the rectum were extracted and automatic rectum segmentation performance was measured in Dice similarity coefficient (DSC). A Gaussian mixture model (GMM) was used to characterize their correlation, and an expectation-maximization (EM) approach was used to solve the corresponding parameter estimation and decouple the impact from air bubbles versus other image attributes based on cluster memberships. Postprostatectomy patient data with high variability in air bubble size and shape were used in this study to reveal the regression relationship. RESULTS: GMM identified two distinct correlative relations between the air-bubble severity in the rectum and the rectum prediction DSC: one showed strong negative dependency of segmentation performance on air bubble presence, and the other one had mild-to-moderate dependency that suggested another group of contributing factors influencing rectum segmentation, such as the inconsistent presence of fiducial seeds and shape extremes. CONCLUSION: The presence of severe air bubbles contributes semilinearly to performance degradation in automatic rectum segmentation. A good correction mechanism may boost the accuracy and consistency of pelvic segmentation.

INTRACRANIAL VESSEL WALL SEGMENTATION FOR ATHEROSCLEROTIC PLAQUE QUANTIFICATION.

Intracranial vessel wall segmentation is critical for the quantitative assessment of intracranial atherosclerosis based on magnetic resonance vessel wall imaging. This work further improves on a previous 2D deep learning segmentation network by the utilization of 1) a 2.5D structure to balance network complexity and regularizing geometry continuity; 2) a UNET++ model to achieve structure adaptation; 3) an additional approximated Hausdorff distance (HD) loss into the objective to enhance geometry conformality; and 4) landing in a commonly used morphological measure of plaque burden - the normalized wall index (NWI) - to match the clinical endpoint. The modified network achieved Dice similarity coefficient of 0.9172 ± 0.0598 and 0.7833 ± 0.0867, HD of 0.3252 ± 0.5071 mm and 0.4914 ± 0.5743 mm, mean surface distance of 0.0940 ± 0.0781 mm and 0.1408 ± 0.0917 mm for the lumen and vessel wall, respectively. These results compare favorably to those obtained by the original 2D UNET on all segmentation metrics. Additionally, the proposed segmentation network reduced the mean absolute error in NWI from 0.0732 ± 0.0294 to 0.0725 ± 0.0333.

Technical Note: An embedding-based medical note de-identification approach with sparse annotation.

PURPOSE: Medical note de-identification is critical for the protection of private information and the security of data sharing in collaborative research. The task demands the complete removal of all patient names and other sensitive information such as addresses and phone numbers from medical records. Accomplishing this goal is challenging, with many variations in the medical note formats and string representations. Existing de-identification approaches include pattern matching where extensive dictionary lists are constructed a prior; and entity tagging, which trains on a large word-wise annotated corpus. This motivates us to study an alternative to the existing approaches with a reduced annotation burden. METHODS: In this work, we propose a novel approach that implicitly accounts for the language territory of sensitive information. Specifically, our approach incorporates a contextualized word embedding module and a multilayer perceptron to simultaneously infer the similarity of sensitive and non-sensitive vocabularies to a constructed landmark set, providing an overall sparsely supervised classification. To demonstrate the rationale, we present the principle and work pipeline with the task of name removal, but the proposed method applies to other strings as well. RESULTS: On a large cohort of hybrid clinical reports, including various forms of consulting, on-treatment-visit, and follow-up notes, we achieved >0.99 accuracies in our constructed training, validation, and testing sets. The sensitivity and specificity were 1.0 and 0.9973, respectively, for two randomly selected reports, comparing favorably to the benchmark Stanford NER tagger, which achieved 0.8529 and 0.9969. The F1 score was 0.889 ± 0.046 and 0.822 ± 0.103 across six randomly selected reports for the proposed method and the Stanford NER, respectively, and the result was significant under a one-sided t-test with alpha = 0.1. CONCLUSION: Our qualitative and quantitative analysis shows that our method achieved better results than the pretrained 3-class Stanford NER toolbox.

Impact of Electrode Number on the Performance of High-Definition Transcranial Direct Current Stimulation (HD-tDCS).

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation for treating brain disorders by applying constant current through scalp towards the targeted cortex regions. Precisely activating or inhibiting a specific area without interfering other parts in the brain is a challenge of tDCS. Recently high-definition tDCS (HD-tDCS) with optimization technique attracts a lot of attention due to the improved focality. Unlike conventional tDCS which utilizes two large pads to deliver current to certain area, HD-tDCS employs tens of smaller electrodes. The purpose of this work is to study the effect of the electrode number on the performance of HD-tDCS. A realistic head model with four layers of tissue was constructed with different electrode montages. A systematic simulation study was conducted using targets in different regions with different functions to analyze the focusing capability, stimulation accuracy, and the intensity of constrained least square based optimized HD-tDCS. Results show that better performance in all three aspects can be achieved by increasing the electrode number.

Improving EEG Source Localization with a Novel Regularization: Spatiotemporal Graph Total Variation (STGTV) Method.

Electroencephalography (EEG) source Iocalization aims at reconstructing the current density on the brain cortex from scalp EEG recordings. It of ten starts with a generative model that maps brain activity to the EEG recording, and then solves the inverse problem. Previously proposed method graph fractional-order total variation (gFOTV) is based on spatial regularization, and was shown superior to some other existing spatial-regularized methods in simulation tests. However, the gFOTV addresses inverse problem for one time point at a time. The resultant estimated times series of brain activity is a simple concatenation of reconstructions independently performed at each time instance, and risks spurious temporal discontinuity due to overfitting noise in EEG recordings. In addition, the performance is subject to low signal-to-noise ratio (SNR) and small number of electrodes, which happens in realistic EEG recordings. To account for the generally continuous temporal variation in brain activity, but also allow for properly triggering abrupt changes, we propose a novel formulation that incorporates spatiotemporal regularization. Specifically, our method, called spatiotemporal graph total variation (STGTV) adopts graph fractional-order total variation (gFOTV) for spatial regularization and total variation (TV) for temporal regularization. The gFOTV encourages spatially smooth source distributions, and the temporal TV enhances temporal consistency in estimated activity maps. The introduction of implicit temporal coupling by temporal TV also helps with noise cancelation and enhances SNR. In a simulation study, the performance of the proposed method was compared against that from the gFOTV regularization alone. The results showed that the proposed STGTV method significantly improved gFOTV, with lower Iocalization errors and less spuriously discovered sources.