Unsupervised OCT image despeckling with ground-truth- and repeated-scanning-free features.

Optical coherence tomography (OCT) can resolve biological three-dimensional tissue structures, but it is inevitably plagued by speckle noise that degrades image quality and obscures biological structure. Recently unsupervised deep learning methods are becoming more popular in OCT despeckling but they still have to use unpaired noisy-clean images or paired noisy-noisy images. To address the above problem, we propose what we believe to be a novel unsupervised deep learning method for OCT despeckling, termed Double-free Net, which eliminates the need for ground truth data and repeated scanning by sub-sampling noisy images and synthesizing noisier images. In comparison to existing unsupervised methods, Double-free Net obtains superior denoising performance when trained on datasets comprising retinal and human tissue images without clean images. The efficacy of Double-free Net in denoising holds significant promise for diagnostic applications in retinal pathologies and enhances the accuracy of retinal layer segmentation. Results demonstrate that Double-free Net outperforms state-of-the-art methods and exhibits strong convenience and adaptability across different OCT images.

SNR-Net OCT: brighten and denoise low-light optical coherence tomography images via deep learning.

Low-light optical coherence tomography (OCT) images generated when using low input power, low-quantum-efficiency detection units, low exposure time, or facing high-reflective surfaces, have low bright and signal-to-noise rates (SNR), and restrict OCT technique and clinical applications. While low input power, low quantum efficiency, and low exposure time can help reduce the hardware requirements and accelerate imaging speed; high-reflective surfaces are unavoidable sometimes. Here we propose a deep-learning-based technique to brighten and denoise low-light OCT images, termed SNR-Net OCT. The proposed SNR-Net OCT deeply integrated a conventional OCT setup and a residual-dense-block U-Net generative adversarial network with channel-wise attention connections trained using a customized large speckle-free SNR-enhanced brighter OCT dataset. Results demonstrated that the proposed SNR-Net OCT can brighten low-light OCT images and remove the speckle noise effectively, with enhancing SNR and maintaining the tissue microstructures well. Moreover, compared to the hardware-based techniques, the proposed SNR-Net OCT can be of lower cost and better performance.

Depth-resolved transverse-plane motion tracking with configurable measurement features via optical coherence tomography.

Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, has become one of the most successful optical technologies implemented in medicine and clinical practice. Here we report a novel technique of depth-resolved transverse-plane motion tracking with configurable measurement features via optical coherence tomography, termed OCT-MT. Based on OCT circular scanning combined with speckle spatial oversampling, the OCT-MT technique can perform depth-resolved transverse-plane motion tracking. Benefitting from the optical interference and depth-resolved feature, the proposed OCT-MT can reduce the requirements on the input power of the irradiation signal and the surface reflectivity and roughness of the target, when performing motion tracking. Furthermore, OCT-MT can conduct such kind of motion tracking with configurable measurement ranges and resolutions by configuring A-line number per scanning circle, circular scanning radius, and A-line scanning time. The proposed OCT-MT technique may expand the ability of motion tracking for OCT in addition to imaging.

Hybrid-structure network and network comparative study for deep-learning-based speckle-modulating optical coherence tomography.

Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, can resolve sample three-dimensional microstructures. However, speckle noise imposes obvious limitations on OCT resolving capabilities. Here we proposed a deep-learning-based speckle-modulating OCT based on a hybrid-structure network, residual-dense-block U-Net generative adversarial network (RDBU-Net GAN), and further conducted a comprehensively comparative study to explore multi-type deep-learning architectures' abilities to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This is the first time that network comparative study has been performed on a customized dataset containing mass more-general speckle patterns obtained from a custom-built speckle-modulating OCT, but not on retinal OCT datasets with limited speckle patterns. Results demonstrated that the proposed RDBU-Net GAN has a more excellent ability to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This work will be useful for future studies on OCT speckle removing and deep-learning-based speckle-modulating OCT.

Sm-Net OCT: a deep-learning-based speckle-modulating optical coherence tomography.

Speckle imposes obvious limitations on resolving capabilities of optical coherence tomography (OCT), while speckle-modulating OCT can efficiently reduce speckle arbitrarily. However, speckle-modulating OCT seriously reduces the imaging sensitivity and temporal resolution of the OCT system when reducing speckle. Here, we proposed a deep-learning-based speckle-modulating OCT, termed Sm-Net OCT, by deeply integrating conventional OCT setup and generative adversarial network trained with a customized large speckle-modulating OCT dataset containing massive speckle patterns. The customized large speckle-modulating OCT dataset was obtained from the aforementioned conventional OCT setup rebuilt into a speckle-modulating OCT and performed imaging using different scanning parameters. Experimental results demonstrated that the proposed Sm-Net OCT can effectively obtain high-quality OCT images without the electronic noise and speckle, and conquer the limitations of reducing the imaging sensitivity and temporal resolution which conventional speckle-modulating OCT has. The proposed Sm-Net OCT can significantly improve the adaptability and practicality capabilities of OCT imaging, and expand its application fields.

Detection and compensation of dispersion mismatch for frequency-domain optical coherence tomography based on A-scan's spectrogram.

Balanced dispersion between reference and sample arms is critical in frequency-domain optical coherence tomography (FD-OCT) to perform imaging with the optimal axial resolution, and the spectroscopic analysis of each voxel in FD-OCT can provide the metric of the spectrogram. Here we revisited dispersion mismatch in the spectrogram view using the spectroscopic analysis of voxels in FD-OCT and uncovered that the dispersion mismatch disturbs the A-scan's spectrogram and reshapes the depth-resolved spectra in the spectrogram. Based on this spectroscopic effect of dispersion mismatch on A-scan's spectrogram, we proposed a numerical method to detect dispersion mismatch and perform dispersion compensation for FD-OCT. The proposed method can visually and quantitatively detect and compensate for dispersion mismatch in FD-OCT, with visualization, high sensitivity, and independence from sample structures. Experimental results of tape and mouse eye suggest that this technique can be an effective method for the detection and compensation of dispersion mismatch in FD-OCT.

Multiple aperture synthetic optical coherence tomography for biological tissue imaging.

An inherent compromise must be made between transverse resolution and depth of focus (DOF) in spectral domain optical coherence tomography (SD-OCT). Thus far, OCT has not been capable of providing a sufficient DOF to stably acquire cellular-resolution images. We previously reported a novel technique named multiple aperture synthesis (MAS) to extend the DOF in high-resolution OCT [Optica4, 701 (2017)]. In this technique, the illumination beam is scanned across the objective lens pupil plane by being steered at the pinhole using a custom-made microcylindrical lens. Images captured via multiple distinctive apertures were digitally refocused, which is similar to synthetic aperture radar. In this study, we applied this technique for the first time to image both a homemade microparticle sample and biological tissue. The results demonstrated the feasibility and efficacy of high-resolution biological tissue imaging with a dramatic DOF extension.

Leukocyte recognition in human fecal samples using texture features.

Unlike urine or blood samples with a single background, human fecal samples contain large amounts of food debris, amorphous particles, and undigested plant cells. It is difficult to segment such impurities when mixed with leukocytes. Cell degradation results in ambiguous nuclei, incompleteness of the cell membrane, and a changeable cell morphology, which are difficult to recognize. Aiming at the segmentation problem, a threshold segmentation method combining an inscribed circle and circumscribed circle is proposed to effectively remove the adhesion impurities with a segmentation accuracy reaching 97.6%. For the identification problem, five texture features (i.e., LBP-uniform, Gabor, HOG, GLCM, and Haar) were extracted and classified using four kinds of classifiers (support vector machine (SVM), artificial neural network, AdaBoost, and random forest). The experimental results show that using a histogram of oriented gradient features with an SVM classifier can achieve precision of 88.46% and recall of 88.72%.

Automatic detection of trichomonads based on an improved Kalman background reconstruction algorithm.

Automatic detection of trichomonads in leukorrhea provides important information for evaluating gynecological diseases. Traditional manual microscopy, which depends on the operator's expertise and subjective factors, has high false-positive rates (i.e., low specificity) and low efficiency. To date, there are many detection methods for biological cells based on morphological characteristics. However, the morphology of trichomonads changes, and its size is not fixed; moreover, they are similar to human leukocytes. Therefore, it is difficult to classify trichomonads based on morphological characteristics. In this study, a moving object detection method based on an improved Kalman background reconstruction algorithm is proposed to detect trichomonads automatically, considering the dynamic characteristics of trichomonads at room temperature. The experimental results show that the trichomonads can be accurately identified, and the phenomena of tailing and ghosts are eliminated. Furthermore, this algorithm easily adapts to continuous or sudden changes in light, focal length variation, and the impact of lens shift, and it has good robustness and only a moderate amount of calculation burden.

Automatic identification of fungi in microscopic leucorrhea images.

Identifying fungi in microscopic leucorrhea images provides important information for evaluating gynecological diseases. Subjective judgment and fatigue can greatly affect recognition accuracy. This paper proposes an automatic identification system to detect fungi in leucorrhea images that incorporates a convolutional neural network, the histogram of oriented gradients algorithm, and a binary support vector machine. In experiments, the detection rate of the positive samples was as high as 99.8%. The experimental results demonstrate the effectiveness of the proposed method and its potential as a primary software component of a completely automated system.

Automatic Identification of Human Erythrocytes in Microscopic Fecal Specimens.

Traditional fecal erythrocyte detection is performed via a manual operation that is unsuitable because it depends significantly on the expertise of individual inspectors. To recognize human erythrocytes automatically and precisely, automatic segmentation is very important for extraction of characteristics. In addition, multiple recognition algorithms are also essential. This paper proposes an algorithm based on morphological segmentation and a fuzzy neural network. The morphological segmentation process comprises three operational steps: top-hat transformation, Otsu's method, and image binarization. Following initial screening by area and circularity, fuzzy c-means clustering and the neural network algorithms are used for secondary screening. Subsequently, the erythrocytes are screened by combining the results of five images obtained at different focal lengths. Experimental results show that even when the illumination, noise pollution, and position of the erythrocytes are different, they are all segmented and labeled accurately by the proposed method. Thus, the proposed method is robust even in images with significant amounts of noise.

Toward Ground-Truth Optical Coherence Tomography via Three-Dimensional Unsupervised Deep Learning Processing and Data.

Optical coherence tomography (OCT) can perform non-invasive high-resolution three-dimensional (3D) imaging and has been widely used in biomedical fields, while it is inevitably affected by coherence speckle noise which degrades OCT imaging performance and restricts its applications. Here we present a novel speckle-free OCT imaging strategy, named toward-ground-truth OCT ( t GT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed t GT-OCT utilizes an unsupervised 3D-convolution deep-learning network trained using random 3D volumetric data to distinguish and separate speckle from real structures in 3D imaging volumetric space; moreover, t GT-OCT effectively further reduces speckle noise and reveals structures that would otherwise be obscured by speckle noise while preserving spatial resolution. Results derived from different samples demonstrated the high-quality speckle-free 3D imaging performance of t GT-OCT and its advancement beyond the previous state-of-the-art. The code is available online: https://github.com/Voluntino/tGT-OCT.

High-precision Drosophila heart segmentation and dynamic cardiac parameter measurement for optogenetics-OCT-based cardiac function research.

Drosophila model has been widely used to study cardiac functions, especially combined with optogenetics and optical coherence tomography (OCT) that can continuously acquire mass cross-sectional images of the Drosophila heart in vivo over time. It's urgent to quickly and accurately obtain dynamic Drosophila cardiac parameters such as heartbeat rate for cardiac function quantitative analysis through these mass cross-sectional images of the Drosophila heart. Here we present a deep-learning method that integrates U-Net and generative adversarial network architectures while incorporating residually connected convolutions for high-precision OCT image segmentation of Drosophila heart and dynamic cardiac parameter measurements for optogenetics-OCT-based cardiac function research. We compared our proposed network with the previous approaches and our segmentation results achieved the accuracy of intersection over union and Dice similarity coefficient higher than 98%, which can be used to better quantify dynamic heart parameters and improve the efficiency of Drosophila-model-based cardiac research via the optogenetics-OCT-based platform.

Multi-type maternal diabetes mellitus affects human placental villous geometric morphology: A three-dimensional imaging study.

INTRODUCTION: Diabetes mellitus leads to maldevelopment of the villous morphology in the human placenta, disrupting the exchange of materials between the maternal and fetal compartments, consequently compromising fetal development. This study aims to explore how different types of diabetes mellitus affect human placental villous geometric morphology including branching numbers and sizes (length, diameter). METHODS: Here an optical coherence tomography (OCT)-based 3D imaging platform was utilized to capture 3D images of placental villi from different types of diabetes, including type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), and gestational diabetes mellitus (GDM). RESULTS: Different types of diabetes mellitus exhibit different effects on human placental villous geometric morphological parameters: GDM had greater placenta villous parameters at intermediate villous diameter (IVD), terminal villous diameter (TVD), terminal villous length (TVL) compared to the healthy, T1DM, and T2DM, and these differences were statistically significant. The TVD of T1DM and T2DM had significantly greater sizes than the healthy. There was no statistically significant difference in the number of villous branches among the three types of diabetes, but T1DM and GDM had more villous branches than healthy individuals. DISCUSSION: Diabetes mellitus affects the geometric morphology of human placental villi, with varying effects observed in pregnancies of different diabetes types. These findings offer a novel avenue for exploring underlying pathophysiological mechanisms and enhancing the management of women with diabetes from preconception through pregnancy.

High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention.

Wet age-related macular degeneration (AMD) is the leading cause of visual impairment and vision loss in the elderly, and optical coherence tomography (OCT) enables revolving biotissue three-dimensional micro-structure widely used to diagnose and monitor wet AMD lesions. Many wet AMD segmentation methods based on deep learning have achieved good results, but these segmentation results are two-dimensional, and cannot take full advantage of OCT's three-dimensional (3D) imaging characteristics. Here we propose a novel deep-learning network characterizing multi-scale and cross-channel feature extraction and channel attention to obtain high-accuracy 3D segmentation results of wet AMD lesions and show the 3D specific morphology, a task unattainable with traditional two-dimensional segmentation. This probably helps to understand the ophthalmologic disease and provides great convenience for the clinical diagnosis and treatment of wet AMD.

Three-dimensional morphological revealing of human placental villi with common obstetric complications via optical coherence tomography.

Placental villi play a vital role in human fetal development, acting as the bridge of material exchange between the maternal and fetal. The abnormal morphology of placental villi is closely related to placental circulation disorder and pregnancy complications. Revealing placental villi three-dimensional (3D) morphology of common obstetric complications and healthy pregnancies provides a new perspective for studying the role of the placenta and its villi in the development of pregnancy diseases. In this study, we established a noninvasive, high-resolution 3D imaging platform via optical coherence tomography to reveal placental villi 3D morphological information of diseased and normal placentae. For the first time, 3D morphologies of placental villous tree structures in common obstetric complications were quantitatively revealed and corresponding 3D information could visualize the morphological characteristics of the placental villous tree from a more intuitive perspective, providing helpful information to the study of fetal development, feto-maternal material exchange, and gestational complications treatment.

MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.

High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.

SDoF-Net: Super Depth of Field Network for Cell Detection in Leucorrhea Micrograph.

Accompanied with the rapid increase of the demand for routine examination of leucorrhea, efficiency and accuracy become the primary task. However, in super depth of field (SDoF) system, the problem of automatic detection and localization of cells in leucorrhea micro-images is still a big challenge. The changing of the relative position between the cell center and focus plane of microscope lead to variable cell morphological structure in the two-dimensional image, which is an important reason for the low accuracy of current deep learning target detection algorithms. In this paper, an object detection method based on Retinanet in state of super depth of field is proposed, which can achieve high precision detecting of leucorrhea components by the SDoF feature aggregation module. Compared with the current mainstream algorithms, the mean average accuracy (mAP) index has been improved significantly, the mAP index is 82.7% for SDoF module and 83.0% for SDoF+ module, with an average increase of more than 10%. These improved features can significantly improve the efficiency and accuracy of the algorithm. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system.

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

Fecal samples can easily be collected and are representative of a person's current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic analysis is needed. Nevertheless, recognition exhaustion time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell-detection algorithm based on the Faster-R-CNN technique: the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis are proposed for cell location and recognition in microscopic images. Our algorithm achieved a mean average precision of 84% and a 723 ms detection time per sample for 40,560 fecal images. Thus, this approach may provide a solid theoretical basis for real-time detection in routine clinical examinations while accelerating the process to satisfy increasing demand.

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U-Net with Attention Mechanism.

The vestibular system is the sensory apparatus that helps the body maintain its postural equilibrium, and semicircular canal is an important organ of the vestibular system. The semicircular canals are three membranous tubes, each forming approximately two-thirds of a circle with a diameter of approximately 6.5 mm, and segmenting them accurately is of great benefit for auxiliary diagnosis, surgery, and treatment of vestibular disease. However, the semicircular canal has small volume, which accounts for less than 1% of the overall computed tomography image. Doctors have to annotate the image in a slice-by-slice manner, which is time-consuming and labor-intensive. To solve this problem, we propose a novel 3D convolutional neural network based on 3D U-Net to automatically segment the semicircular canal. We added the spatial attention mechanism of 3D spatial squeeze and excitation modules, as well as channel attention mechanism of 3D global attention upsample modules to improve the network performance. Our network achieved an average dice coefficient of 92.5% on the test dataset, which shows competitive performance in semicircular canals segmentation task.