Semi-supervised 3D retinal fluid segmentation via correlation mutual learning with global reasoning attention.

Accurate 3D segmentation of fluid lesions in optical coherence tomography (OCT) is crucial for the early diagnosis of diabetic macular edema (DME). However, higher-dimensional spatial complexity and limited annotated data present significant challenges for effective 3D lesion segmentation. To address these issues, we propose a novel semi-supervised strategy using a correlation mutual learning framework for segmenting 3D DME lesions from 3D OCT images.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Self-assembling human heart organoids for the modeling of cardiac development and congenital heart disease.

Congenital heart defects constitute the most common human birth defect, however understanding of how these disorders originate is limited by our ability to model the human heart accurately in vitro. Here we report a method to generate developmentally relevant human heart organoids by self-assembly using human pluripotent stem cells. Our procedure is fully defined, efficient, reproducible, and compatible with high-content approaches.

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.

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.

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.

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation.

Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images.

Understanding optical reflectance contrast for real-time characterization of epithelial precursor lesions.

Detecting early-stage epithelial cancers and their precursor lesions are challenging as lesions could be subtle and focally or heterogeneously distributed over large mucosal areas. Optical coherence tomography (OCT) that enables wide-field imaging of subsurface microstructures in vivo is a promising screening tool for epithelial diseases. However, its diagnostic capability has not been fully appreciated since the optical reflectance contrast is poorly understood.

Geometry-Dependent Spectroscopic Contrast in Deep Tissues.

Nano-structures of biological systems can produce diverse spectroscopic effects through interactions with broadband light. Although structured coloration at the surface has been extensively studied, natural spectroscopic contrasts in deep tissues are poorly understood, which may carry valuable information for evaluating the anatomy and function of biological systems. Here we investigated the spectroscopic characteristics of an important geometry in deep tissues at the nanometer scale: packed nano-cylinders, in the near-infrared window, numerically predicted and experimentally proved that transversely oriented and regularly arranged nano-cylinders could selectively backscatter light of the long wavelengths.

Automatic classification of cells in microscopic fecal images using convolutional neural networks.

The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed.

Trichomonas Detection in Leucorrhea Based on VIBE Method.

Trichomonas examination is one of the important items in the leucorrhea routine detection. And it cannot be recognized by still images because of the unstable morphology and unfixed focal location caused by motion characteristic. We proposed an improved VIBE algorithm.

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.

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)].

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.