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.

The microcharacter recognition on the distributed feedback (DFB) laser chip is critically essential but a challenging task for the quality control in the incoming chip inspection and optical device manufacturing lines. Hence, this article proposes, designs, and implements a micro-optical character recognition system based on the microscopic imaging system and deep convolutional neural network to facilitate the microcharacter recognition of DFB chips. The proposed micron optical character recognition (MOCR) contains four components, namely, a microscopic imaging system-based visual-based measurement (VBM), character detection, orientation correction, and character recognition. In particular, data augmentation methods considering limited datasets and some chip character features are studied so as to accommodate complex industrial environments and tiny DFB chips. Experiments from the DFB image dataset of a real optical device manufacturing line showed that it reached the accuracy of 98.8%, which took a high speed of over 12.8 fps coping with DFB chip images, and saved 72% of the labor in the inspection position compared to traditional methods. To the best of our knowledge, there are no existing reports focused on MOCR on DFB chips. So, the first realization and successful application of MOCR played a key role in the intelligent manufacturing and quality traceability of DFB chips and devices in the field of optical communications, which is also of great significance for further accelerating the development of the industrial Internet.

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.

Quantitative phase imaging and measurement of surface topography and fluid dynamics for objects, especially for moving objects, is critical in various fields. Although effective, existing synchronous phase-shifting methods may introduce additional phase changes in the light field due to differences in optical paths or need specific optics to implement synchronous phase-shifting, such as the beamsplitter with additional anti-reflective coating and a micro-polarizer array. Therefore, we propose a synchronous phase-shifting method based on the Mach–Zehnder interferometer to tackle these issues in existing methods. The proposed method uses common optics to simultaneously acquire four phase-shifted digital holograms with equal optical paths for object and reference waves. Therefore, it can be used to reconstruct the phase distribution of static and dynamic objects with high precision and high resolution. In the experiment, the theoretical resolution of the proposed system was 1.064 µm while the actual resolution could achieve 1.381 µm, which was confirmed by measuring a phase-only resolution chart. Besides, the dynamic phase imaging of a moving standard object was completed to verify the proposed system’s effectiveness. The experimental results show that our proposed method is suitable and promising in dynamic phase imaging and measurement of moving objects using phase-shifting digital holography.

The intelligent recognition of formed elements in microscopic images is a research hotspot. Whether the microscopic image is clear or blurred is the key factor affecting the recognition accuracy. Microscopic images of human feces contain numerous items, such as undigested food, epithelium, bacteria and other formed elements, leading to a complex image composition. Consequently, traditional image quality assessment (IQA) methods cannot accurately assess the quality of fecal microscopic images or even identify the clearest image in the autofocus process. In response to this difficulty, we propose a blind IQA method based on a deep convolutional neural network (CNN), namely GMANet. The gradient information of the microscopic image is introduced into a low-level convolutional layer of the CNN as a mask attention mechanism to force high-level features to pay more attention to sharp regions. Experimental results show that the proposed network has good consistency with human visual properties and can accurately identify the clearest microscopic image in the autofocus process. Our proposed model, trained on fecal microscopic images, can be directly applied to the autofocus process of leucorrhea and blood samples without additional transfer learning. Our study is valuable for the autofocus task of microscopic images with complex compositions.

Diagnosis of Trichomonas vaginalis infection is one of the most important factors in the routine examination of leucorrhea. According to the motion characteristics of Trichomonas vaginalis, a viable detection method is the use of a microscopic camera to record videos of leucorrhea samples and video object detection algorithms for detection. Most Trichomonas vaginalis is defocused and displays as shadow regions on microscopic images, and it is hard to recognize the movement of shadow regions using traditional video object detection algorithms. In order to solve this problem, we propose two convolutional neural networks based on an encoder-decoder architecture. The first network has the ability to learn the difference between frames and utilizes the image and optical flow information of three consecutive frames as the input to perform rough detection. The second network corrects the coarse contours and uses the image information and the rough detection result of the current frame as the input to perform fine detection. With these two networks applied, the metric value of the mean intersection over union of Trichomonas vaginalis achieves 72.09% on test videos. The proposed networks can effectively detect defocused Trichomonas vaginalis and suppress false alarms caused by the motion of formed elements or impurities.