Application of Advanced Imaging to the Study of Virus-Host Interactions.

Recent advances in light and electron microscopy are uncovering viral lifecycle events with a level of detail never before seen [...].

Serial imaging of micro-agents and cancer cell spheroids in a microfluidic channel using multicolor fluorescence microscopy.

Multicolor fluorescence microscopy is a powerful technique to fully visualize many biological phenomena by acquiring images from different spectrum channels. This study expands the scope of multicolor fluorescence microscopy by serial imaging of polystyrene micro-beads as surrogates for drug carriers, cancer spheroids formed using HeLa cells, and microfluidic channels. Three fluorophores with different spectral characteristics are utilized to perform multicolor microscopy. According to the spectrum analysis of the fluorophores, a multicolor widefield fluorescence microscope is developed. Spectral crosstalk is corrected by exciting the fluorophores in a round-robin manner and synchronous emitted light collection. To report the performance of the multicolor microscopy, a simplified 3D tumor model is created by placing beads and spheroids inside a channel filled with the cell culture medium is imaged at varying exposure times. As a representative case and a method for bio-hybrid drug carrier fabrication, a spheroid surface is coated with beads in a channel utilizing electrostatic forces under the guidance of multicolor microscopy. Our experiments show that multicolor fluorescence microscopy enables crosstalk-free and spectrally-different individual image acquisition of beads, spheroids, and channels with the minimum exposure time of 5.5 ms. The imaging technique has the potential to monitor drug carrier transportation to cancer cells in real-time.

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study.

There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively included DWI data of patients with acute ischemic lesions from six centers. Dataset A (n = 2986) and B (n = 3951) included data from Siemens and GE MRI scanners, respectively. The datasets were split into the training (80%), validation (10%), and internal test (10%) sets, and six neuroradiologists created ground-truth masks. Models A and B were the proposed neural networks trained on datasets A and B. The models subsequently fine-tuned across the datasets using their validation data. Another radiologist performed the segmentation on the test sets for comparisons. The median Dice scores of models A and B were 0.858 and 0.857 for the internal tests, which were non-inferior to the radiologist's performance, but demonstrated lower performance than the radiologist on the external tests. Fine-tuned models A and B achieved median Dice scores of 0.832 and 0.846, which were non-inferior to the radiologist's performance on the external tests. The present work shows that the inter-vendor operability of deep learning for the segmentation of ischemic lesions on DWI might be enhanced via transfer learning; thereby, their clinical applicability and generalizability could be improved.

Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study.

AIMS: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)-empowered microscope to improve Ki67 scoring concordance. METHODS AND RESULTS: We developed an AI-empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67-stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI-empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI-empowered microscope. CONCLUSION: The AI-empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment.

Validation of Whole Slide Imaging for primary surgical pathology diagnosis of prostate biopsies.

CONTEXT: Whole slide imaging (WSI) is an important component of digital pathology which includes digitization of glass slides and their storage as digital images. Implementation of WSI for primary surgical pathology diagnosis is evolving, following various studies which have evaluated the feasibility of WSI technology for primary diagnosis. AIMS, SETTINGS AND DESIGN: The present study was a single-center, observational study which included evaluation by three pathologists and aimed at assessing concordance on specialty-specific diagnosis and comparison of time taken for diagnosis on WSI and conventional light microscopy (CLM). MATERIALS AND METHODS: Seventy prostate core biopsy slides (reported between January 2016 and December 2016) were scanned using Pannoramic MIDI II scanner, 3DHISTECH, Budapest, Hungary, at 20× and 40×. Sixty slides were used for validation study following training with 10 slides. STATISTICAL ANALYSIS USED: Intraobserver concordance for diagnosis between the two platforms of evaluation was analyzed using Cohen's κ statistics and intraclass correlation coefficient (ICC); observation time for diagnosis was compared by Wilcoxon signed-rank test. RESULTS: Interpretation on WSI using 20× and 40× was comparable with no major discordance. A high level of intraobserver agreement was observed between CLM and WSI for all three observers, both for primary diagnosis (κ = 0.9) and Grade group (κ = 0.7-0.8) in cases of prostatic adenocarcinoma. The major discordance rate between CLM and WSI was 3.3%-8.3%, which reflected the expertise of the observers. The time spent for diagnosis using WSI was variable for the three pathologists. CONCLUSION: WSI is comparable to CLM and can be safely incorporated for primary histological diagnosis of prostate core biopsies.

Improved SSD network for accurate detection of optic disc and fovea and application in excyclotropia screening.

The detection of the optic disc (OD) and fovea is essential to many automatic diagnosis systems for retinal diseases. The single shot multibox detector (SSD) can generate predictions from feature maps of various resolutions, which has not been introduced into the OD and fovea detection. To enhance the detection performance, we propose an improved SSD network, which has strengthened information flow enabled by the dense connections. The proposed method can achieve multiscale detection of the OD and fovea with strengthened feature propagation. Extensive experiments on the publicly available Messidor database and local fundus images are performed to evaluate the performance of the proposed method. Compared with seven types of representative solutions in the Messidor database, the proposed method can achieve competitive performance compared to state-of-the-art algorithms. Furthermore, the proposed method is applied to the excyclotropia screening. The screening results demonstrate promising application prospects for the proposed method in medical practice.

MicroED Sample Preparation and Data Collection For Protein Crystals.

Microcrystal Electron Diffraction (MicroED) enables structure determination of very small crystals that are much too small to be of use for other conventional diffraction techniques. MicroED has been used to determine the structures of many proteins and small organic molecules, and the technique can be performed on most standard cryo-TEM instruments equipped with high-speed detectors capable of collecting electron diffraction data. Here, we present protocols for MicroED sample preparation and data collection for protein microcrystals.

Photon-counting x-ray detectors for CT.

The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

The Ability to Look: Management of Breast Disease in the Democratic Republic of the Congo Using Smart Ultrasound Technology.

BACKGROUND: The vast majority of women with breast cancer in sub-Saharan Africa present with advanced stage disease, due primarily to the lack of opportunities for early detection and treatment. As part of a larger effort to increase access to diagnostic and therapeutic services for women's cancers in the Democratic Republic of Congo (DRC), we implemented a curriculum to train the local workforce and a program to build the supportive infrastructure for the diagnosis and treatment of breast cancer at a private sector health facility (Biamba Marie Mutombo Hospital) in Kinshasa. STUDY DESIGN: After onsite trainings in the DRC by a US breast surgeon (RT), Congolese surgeons, general physicians, physician assistants, and nurses used the Phillips Lumify smart-phone ultrasound device to perform and interpret the results of whole breast ultrasound on symptomatic women. Surgeons were trained to perform ultrasound-guided core needle biopsy on those who met the criteria for tissue diagnosis, after which they trained nurses to do the same. RESULTS: Over 3 years, 5,211 patients were identified as having a breast abnormality on clinical breast examination. Ultrasound abnormalities were noted in 1,493 (27%) patients, of which 632 (42%) met the criteria for ultrasound-guided core needle biopsy or fine needle aspiration. Pathology reports were available on 368 (58%) patients who underwent biopsy, of which 164 were malignant and 204 benign. CONCLUSIONS: We demonstrated how the "ability to look" using smart technology can be successfully used to augment clinical breast exam and triage patients for biopsy in a resource-constrained African setting.

Objective evaluation of immediate reading skin prick test applying image planimetric and reaction thermometry analyses.

The skin prick test is used to diagnose patients' sensitization to antigens through a mediated IgE response. It is a practical and quick exam, but its diagnosis depends on instruments for measuring the allergic response and observer's interpretation. The conventional method for inferring about the allergic reaction is performed from the dimensions of the wheals, which are measured using a ruler or a caliper. To make this diagnosis less dependent on human interpretation, the present study proposes two alternative methods to infer about the allergic reaction: computational determination of the wheal area and a study of the temperature variation of the patient's skin in the puncture region. For this purpose, prick test using histamine was performed on 20 patients randomly selected. The areas were determined by the conventional method using the dimensions of the wheals measured with a digital caliper 30 min after the puncture. The wheal areas were also determined by a Python algorithm using photographs of the puncture region obtained with a smartphone. A variable named circularity deviation was also determined for each analyzed wheal. The temperature variation was monitored using an infrared temperature sensor, which collected temperature data for 30 min. All results were statistically compared or correlated. The results showed that the computational method to infer the wheal areas did not differ significantly from the areas determined by the conventional method (p-value = 0.07585). Temperature monitoring revealed that there was a consistent temperature increase in the first minutes after the puncture, followed by stabilization, so that the data could be adjusted by a logistic equation (R2 = 0.96). This adjustment showed that the optimal time to measure the temperature is 800 s after the puncture, when the temperature stabilization occurs. The results have also shown that this temperature stabilization has a significant positive correlation with wheal area (p-value = 0.0015). Thus, we concluded that the proposed computational method is more accurate to infer the wheal area when compared to the traditional method, and that the temperature may be used as an alternative parameter to infer about the allergic reaction.

Artificial Intelligence Medical Ultrasound Equipment: Application of Breast Lesions Detection.

Breast cancer ranks first among cancers affecting women's health. Our work aims to realize the intelligence of the medical ultrasound equipment with limited computational capability, which is used for the assistant detection of breast lesions. We embed the high-computational deep learning algorithm into the medical ultrasound equipment with limited computational capability by two techniques: (1) lightweight neural network: considering the limited computational capability of ultrasound equipment, a lightweight neural network is designed, which greatly reduces the amount of calculation. And we use the technique of knowledge distillation to train the low-precision network helped with the high-precision network; (2) asynchronous calculations: consider four frames of ultrasound images as a group; the image of the first frame of each group is used as the input of the network, and the result is respectively fused with the images of the fourth to seventh frames. An amount of computation of 30 GFLO/frame is required for the proposed lightweight neural network, about 1/6 of that of the large high-precision network. After trained from scratch using the knowledge distillation technique, the detection performance of the lightweight neural network (sensitivity = 89.25%, specificity = 96.33%, the average precision [AP] = 0.85) is close to that of the high-precision network (sensitivity = 98.3%, specificity = 88.33%, AP = 0.91). By asynchronous calculation, we achieve real-time automatic detection of 24 fps (frames per second) on the ultrasound equipment. Our work proposes a method to realize the intelligence of the low-computation-power ultrasonic equipment, and successfully achieves the real-time assistant detection of breast lesions. The significance of the study is as follows: (1) The proposed method is of practical significance in assisting doctors to detect breast lesions; (2) our method provides some practical and theoretical support for the development and engineering of intelligent equipment based on artificial intelligence algorithms.

Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial.

BACKGROUND & AIMS: One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. We performed a multicenter, randomized trial to assess the safety and efficacy of a CADe system in detection of colorectal neoplasias during real-time colonoscopy. METHODS: We analyzed data from 685 subjects (61.32 ± 10.2 years old; 337 men) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or workup due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at 3 centers in Italy from September through November 2019. Patients were randomly assigned (1:1) to groups who underwent high-definition colonoscopies with the CADe system or without (controls). The CADe system included an artificial intelligence-based medical device (GI-Genius, Medtronic) trained to process colonoscopy images and superimpose them, in real time, on the endoscopy display a green box over suspected lesions. A minimum withdrawal time of 6 minutes was required. Lesions were collected and histopathology findings were used as the reference standard. The primary outcome was adenoma detection rate (ADR, the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, non-neoplastic resection rate, and withdrawal time. RESULTS: The ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% confidence interval [CI], 1.14-1.45). Adenomas detected per colonoscopy were significantly higher in the CADe group (mean, 1.07 ±1.54) than in the control group (mean 0.71 ± 1.20) (incidence rate ratio, 1.46; 95% CI, 1.15-1.86). Adenomas 5 mm or smaller were detected in a significantly higher proportion of subjects in the CADe group (33.7%) than in the control group (26.5%; RR, 1.26; 95% CI, 1.01-1.52), as were adenomas of 6 to 9 mm (detected in 10.6% of subjects in the CADe group vs 5.8% in the control group; RR, 1.78; 95% CI, 1.09-2.86), regardless of morphology or location. There was no significant difference between groups in withdrawal time (417 ± 101 seconds for the CADe group vs 435 ± 149 for controls; P = .1) or proportion of subjects with resection of non-neoplastic lesions (26.0% in the CADe group vs 28.7% of controls; RR, 1.00; 95% CI, 0.90-1.12). CONCLUSIONS: In a multicenter, randomized trial, we found that including CADe in real-time colonoscopy significantly increases ADR and adenomas detected per colonoscopy without increasing withdrawal time. ClinicalTrials.gov no: 04079478.

Microscopia confocal ex vivo con método de fusión y tinción digital: cambiando paradigmas en el diagnóstico histológico / Ex Vivo Confocal Microscopy Using Fusion Mode and Digital Staining: Changing Paradigms in Histological Diagnosis

La microscopia confocal ex vivo es un sistema de procesamiento de tejidos que permite el análisis histológico inmediato de tejidos extirpados por medio de láseres diodo con distintos espectros de longitud de onda. En estudios previos la microscopia confocal ex vivo reduce el tiempo de análisis de márgenes de forma muy notable, con una sensibilidad y especificidad respecto a la histopatología del 88% y 99%, respectivamente. Recientemente se ha desarrollado una nueva tecnología que es capaz de proveer imágenes digitales con mayor velocidad y mejor resolución que las obtenidas en los dispositivos previos. Mediante el método de fusión (láseres de fluorescencia y reflectancia que escanean simultáneamente), se reproduce con una tinción digital la hematoxilina y eosina de forma inmediata en cada imagen. La implementación de esta nueva tecnología ha abierto definitivamente una puerta en el diagnóstico inmediato de tejidos

The ex vivo confocal microscope is an imaging system designed to analyze freshly excised tissue using two diode lasers with different wavelengths. The technique can dramatically reduce margin analysis times and offers a sensitivity of 88% and a specificity of 89% relative to histopathology. A new technology has recently been developed that produces images more quickly and with a higher resolution than before. By means of a fusion mode that combines simultaneously scanned fluorescence and reflectance images, it produces digitally stained images that simulate the effect of hematoxylin-eosin staining. Application of this new technology has opened the door to real-time tissue diagnostics

Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm.

The detection of objects concealed under people's clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.

A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods.

Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human factors. Proposed hybrid system consists of two progressive steps: automatic segmentation of possible sperm shapes and classification of normal/ab-normal sperms. In the segmentation step, clustering techniques with/without group sparsity approach were tested to extract region of interests from the images. Subsequently, a novel publicly available morphological sperm image data set, whose labels were identified by experts as non-sperm, normal and abnormal sperm, was created as the ground truths of classification step. In the classification step, conventional and ensemble machine learning methods were applied to domain-specific features that were extracted by using wavelet transform and descriptors. Additionally, as an alternative to conventional features, three deep neural network architectures, which can extract high-level features from raw images after using statistical learning, were employed to increase the proposed method's performance. The results show that, for the conventional features, the highest classification accuracies were achieved as 80.5% and 83.8% by using the wavelet- and descriptor-based features that were fed to the Support Vector Machines respectively. On the other hand, the Mobile-Net, which is a very convenient network for smartphones, achieved 87% accuracy. In the light of obtained results, it is seen that a fully automatic hybrid system, which uses the group sparsity to enhance segmentation performance and the Mobile-Net to obtain high-level robust features, can be an effective mobile solution for the sperm morphology analysis problem. A fully automated hybrid human sperm detection and classification system based on mobile-net.

Ultrasound-driven cardiac MRI.

PURPOSE: One of the challenges of cardiac MR imaging is the compensation of respiratory motion, which causes the heart and the surrounding tissues to move. Commonly-used methods to overcome this effect, breath-holding and MR navigation, present shortcomings in terms of available acquisition time or need to periodically interrupt the acquisition, respectively. In this work, an implementation of respiratory motion compensation that obtains information from abdominal ultrasound and continuously adapts the imaged slice position in real time is presented. METHODS: A custom workflow was developed, comprising an MR-compatible ultrasound acquisition system, a feature-motion-tracking system with polynomial predictive capability, and a custom MR sequence that continuously adapts the position of the acquired slice according to the tracked position. The system was evaluated on a moving phantom by comparing sharpness and image blurring between static and moving conditions, and in vivo by tracking the motion of the blood vessels of the liver to estimate the cardiac motion. Cine images of the heart were acquired during free breathing. RESULTS: In vitro, the predictive motion correction yielded significantly better results than non-predictive or non-corrected acquisitions (p â‰ª 0.01). In vivo, the predictive correction resulted in an image quality very similar to the breath-hold acquisition, whereas the uncorrected images show noticeable blurring artifacts. CONCLUSION: In this work, the possibility of using ultrasound navigation with tracking for the real-time adaptation of MR imaging slices was demonstrated. The implemented technique enabled efficient imaging of the heart with resolutions that would not be feasible in a single breath-hold.

Chip-Scale Angle-Selective Imager for In Vivo Microscopic Cancer Detection.

We present an image sensor incorporating angle-selective gratings for resolution enhancement in contact imaging applications. Optical structures designed in the CMOS metal layers above each photodiode form the angle-selective gratings that limit the sensor angle of view to ±18 °, rejecting background light and deblurring the image. The imager is based on a high-gain capacitive transimpedance amplifier pixel using a custom 11fF MOM capacitor, achieving [Formula: see text] sensitivity. The pixel includes a leakage current minimization circuit to remove signal-dependent reset switch leakage and the corresponding dark current is [Formula: see text]. The resulting 4.7 mm by 2.25 mm sensor (80 by 36 pixels) is designed specifically for intraoperative cancer imaging in order to solve the pervasive challenge of identifying microscopic residual cancer foci in vivo, where they can be removed. We demonstrate imaging and detection of foci containing less than 200 cancer cells labeled with fluorescent biomarkers in 50 ms with signal-to-noise ratios greater than 15 dB and the detection of microscopic residual tumor in mice models. The absence of large optical elements enables extreme miniaturization, allowing manipulation within a small, morphologically complex, tumor cavity.

Assessing the Use of Incorrectly Scaled Optical Coherence Tomography Angiography Images in Peer-Reviewed Studies: A Systematic Review.

Importance: Individual differences in axial length affect the lateral magnification of in vivo retinal images and as a result can affect the accuracy of quantitative measurements made from these images. As measurements from optical coherence tomography angiography (OCTA) images are becoming increasingly used in the diagnosis and monitoring of a wide range of diseases, evaluating which studies use correctly scaled images is crucial to their interpretation. Objective: To perform a systematic literature review to assess the percentage of articles that report correcting the scale of their OCTA images for individual differences in retinal magnification. Evidence Review: A PubMed (MEDLINE) search was conducted for articles on OCTA retinal imaging published between June 1, 2015, and June 1, 2018. Initial results included 7552 articles. Initial exclusion criteria removed studies of animal models, as well as reviews, letters, replies, comments, and image-based or photographic essays. Articles not written in English and those that required purchase from non-English language websites were excluded. Articles that did not use OCTA for imaging the retina were also excluded. Remaining articles were reviewed in detail to assess whether the OCTA measurements required correct lateral scaling, and if so, whether axial length was reported or used to scale the images. We also determined the number of articles that mentioned the lack of correct lateral scaling as a limitation of the study. Findings: A total of 989 articles were included in the detailed review. Of these, 509 were determined to require correct image scaling for their analyses, but only 41 (8.0%) report measuring and using axial length to correct the lateral scale of their OCTA images. Furthermore, of the 468 articles that did not correctly scale their images, only 18 (3.8%) mentioned this as a limitation to their study. Conclusions and Relevance: These findings suggest that most peer-reviewed articles in PubMed that use quantitative OCTA measurements use incorrectly scaled images. This could call into question the conclusions of such studies and warrants consideration by OCTA manufacturers, physicians, authors, journal reviewers, and journal editors.