ABSTRACT
The number of white blood cells in the leucorrhea microscopic image can indicate the severity of vaginal inflammation. At present, the detection of white blood cells in leucorrhea mainly relies on manual microscopy by medical experts, which is time-consuming, expensive and error-prone. In recent years, some studies have proposed to implement intelligent detection of leucorrhea white blood cells based on deep learning technology. However, such methods usually require manual labeling of a large number of samples as training sets, and the labeling cost is high. Therefore, this study proposes the use of deep active learning algorithms to achieve intelligent detection of white blood cells in leucorrhea microscopic images. In the active learning framework, a small number of labeled samples were firstly used as the basic training set, and a faster region convolutional neural network (Faster R-CNN) training detection model was performed. Then the most valuable samples were automatically selected for manual annotation, and the training set and the corresponding detection model were iteratively updated, which made the performance of the model continue to increase. The experimental results show that the deep active learning technology can obtain higher detection accuracy under less manual labeling samples, and the average precision of white blood cell detection could reach 90.6%, which meets the requirements of clinical routine examination.
ABSTRACT
Objective:To build a gray-level matching template by using the gray level information of the microscopic image of the transverse section of Chinese medicinal materials,in order to realize the automatic recognition of the images of Chinese medicinal materials independent of scale and orientation. Method:By using the embedding method of polyethylene glycol (PEG),the transverse slices of 19 kinds of common rhizomatous medicinal materials were obtained. The images of the slices were taken by digital microscopic imaging technology,and the mosaic grayscale images were obtained by image registration,noise removal and boundary location. The center of the structure of the materials in the images was selected to establish the polar coordinate system, so as to divide grids from the radial and angular directions. By counting the gray information in each grid,the gray information digital matrix that can characterize the microscopic identification characteristics of the materials was obtained. Images in an appropriate sample size was used to train the matrix for generalization of the matrix. The covariance coefficients between the matrix of positive or negative verification sample and the template matrix were calculated to set the best identification parameters. For each medicinal material,80 fan-shaped images were prepared,including 70% of training samples,15% of validation samples and 15% of test samples,and single template and template set were tested with test samples. Result:In the test of 240 images including non-template-set medicinal materials,the correct recognition rate of single-template test was 90.1%,and that of template-set test was 92.5%. Conclusion:This method can well characterize the microscopic identification characteristics of Chinese medicinal materials, with a strong anti-interference ability and less subjective-errors, acquire sample images easily, and provide technical support for the digitization of morphological quality control of Chinese medicinal materials.
ABSTRACT
The particle diameters of active pharmaceutical ingredient (API) and excipients are important factors to the quality of preparations and have great significances in the reverse engineering to brand products and the consistent evaluation of generic drugs. In this study, a novel method was established for particle size determination to identify the selected component and eliminate other interferential particles by comparing the microscopic images before and after fusion caused by controllable heating. Stearic acid (SA) particles in irregular and spherical shape were selected as a typical excipient to demonstrate the methodology, which were identified from the mixed particles based upon its melting characteristics to detect their particle sizes as well as the size distributions. In the same approach, the morphology and particle size of fenofibrate particles as API in tablets were analyzed. The results illustrated that the particle diameters and particle size distributions of the selected components in the mixture of particles can be detected via the hot-melting characteristics under the prerequisite of proper pretreatment to separate selected components from other particles in microscopic field. In conclusion, this research provides a practical approach for the reverse engineering purpose to brand products and the consistent evaluation of generic drugs.
ABSTRACT
Objective To explore the expression and significance of Kiss-1, Ki-67 and VEGF-C in papillary thyroid carcinoma(PTC) and thyroid follicular adenoma (FA).Methods Forty-four cases of PTC and twelve cases of FA paraffin-embedded tissues were used .Immunohistochemical staining and microscopic image analysis technique were used to analyze the expression of Kiss-1, Ki-67 and VEGF-C.Results The integrated optical density (IOD) of Kiss-1, and VEGF-C in the PTC groups was 475.56 ±126.02 and 805.29 ±226.05,respectively.The proliferation index of Ki-67 protein was (3.36 ±1.11) %and the difference between the PTC and FA groups was statistically significant (P<0.05).The IOD of the above two indices was 408.12 ±124.05 and 912.63 ±108.12 in the PTC with lymph node metastasis group , respectively, while the proliferation index of Ki -67 protein was (3.93 ±0.92) % and the difference vs the group without lymph node metastasis was significant ( P <0.05 ) .In the PTC with capsular infiltration group the IOD of above two was 425.58 ±87.38 and 891.37 ±149.36, the proliferation index of Ki -67 protein was (3.79 ±1.09) %and the difference with PTC group without capsular infiltrtion was statistically significant (P<0.05).Linear correlation analysis showed that Ki-67 and VEGF-C were with positively correlated in PTC and FA tissues (P<0.05),while Kiss-1 and Ki-67, VEGF-C were with negatively correlated in PTC and FA tissues (P<0.05).Conclusion Kiss-1, Ki-67 and VEGF-C can facilitate the differential diagnosis of PTC and FA , serving as prognostic indicators in patients with PTC .
ABSTRACT
Background: New methods are needed to assist medical students with active learning during histopathology classes. The built–in digital cameras of cell phones and smart phones have recently been used to capture histopathological images during histopathology classes. We examined how the use of the cameras affected students’ attitudes to classwork.<br>Method: The students were encouraged to capture histopathological images with the digital cameras of cell phones and smart phones. We observed and recorded changes in their learning attitude.<br>Result: The students captured many histopathological images with their digital cameras. They discussed the pathology of the diseases with their instructors while viewing captured images on the phones’ screens. Some students sorted the image files and used them for self–study after class.<br>Conclusion: Active learning is encouraged by allowing medical students to record histopathological images with the built–in digital cameras of cell phones and smart phones during histopathology classes.
ABSTRACT
Objective:This article provided one splicing technology of medical microscopic image which bases on the comer.Methods:Firstly,use the Harris and MIC algorithms to detect comer.Secondly,use the correlation analysis to obtain the matching points.Lastly,do the splicing and fusion according to the matching points.Results:Achieve the images enhancement and stitching.Conclusions:The experiment proved that the technology can obtained the ideal splicing effect when splicing to the medical microscopic image.
ABSTRACT
It's very difficult segment the medical microscopic image for its features such as multi-objective, complicated background, abundant disturbances and noises, little contrast. After comparing the results of several segmentation arithmetics, this paper puts forward an OTSU-based self-adaptation threshold partition algorithm for effective segmentation of medical microscopic image.