Automated classification of liver fibrosis stages using ultrasound imaging.

BACKGROUND: Ultrasound imaging is the most frequently performed for the patients with chronic hepatitis or liver cirrhosis. However, ultrasound imaging is highly operator dependent and interpretation of ultrasound images is subjective, thus well-trained radiologist is required for evaluation. Automated classification of liver fibrosis could alleviate the shortage of skilled radiologist especially in low-to-middle income countries. The purposed of this study is to evaluate deep convolutional neural networks (DCNNs) for classifying the degree of liver fibrosis according to the METAVIR score using US images. METHODS: We used ultrasound (US) images from two tertiary university hospitals. A total of 7920 US images from 933 patients were used for training/validation of DCNNs. All patient were underwent liver biopsy or hepatectomy, and liver fibrosis was categorized based on pathology results using the METAVIR score. Five well-established DCNNs (VGGNet, ResNet, DenseNet, EfficientNet and ViT) was implemented to predict the METAVIR score. The performance of DCNNs for five-level (F0/F1/F2/F3/F4) classification was evaluated through area under the receiver operating characteristic curve (AUC) with 95% confidential interval, accuracy, sensitivity, specificity, positive and negative likelihood ratio. RESULTS: Similar mean AUC values were achieved for five models; VGGNet (0.96), ResNet (0.96), DenseNet (0.95), EfficientNet (0.96), and ViT (0.95). The same mean accuracy (0.94) and specificity values (0.96) were yielded for all models. In terms of sensitivity, EffcientNet achieved highest mean value (0.85) while the other models produced slightly lower values range from 0.82 to 0.84. CONCLUSION: In this study, we demonstrated that DCNNs can classify the staging of liver fibrosis according to METAVIR score with high performance using conventional B-mode images. Among them, EfficientNET that have fewer parameters and computation cost produced highest performance. From the results, we believe that DCNNs based classification of liver fibrosis may allow fast and accurate diagnosis of liver fibrosis without needs of additional equipment for add-on test and may be powerful tool for supporting radiologists in clinical practice.

Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning.

Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are easily affected by various factors, such as climate change, pests, and diseases, resulting in reduced yield and quality. Advances in computer vision in recent years have been widely used for plant disease detection and classification, providing opportunities for early disease detection, and resulting in improvements in agriculture. Particularly, the early and accurate detection of citrus diseases, which are vulnerable to pests, is very important to prevent the spread of pests and reduce crop damage. Research on citrus pest disease is ongoing, but it is difficult to apply research results to cultivation owing to a lack of datasets for research and limited types of pests. In this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites. The constructed dataset was trained, verified, and tested using a model that had undergone five transfer learning steps. All models used in the experiment had an average accuracy of 97% or more and an average f1 score of 96% or more. We built a web application server using the EfficientNet-b0 model, which exhibited the best performance among the five learning models. The built web application tested citrus pest disease using image samples collected from websites other than the self-collected image samples and prepared data, and both samples correctly classified the disease. The citrus pest automatic diagnosis web system using the model proposed in this study plays a useful auxiliary role in recognizing and classifying citrus diseases. This can, in turn, help improve the overall quality of citrus fruits.

Polyp segmentation with consistency training and continuous update of pseudo-label.

Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervised methods and suitably take advantage of unlabeled data to improve the performance of polyp image segmentation. First, we propose an encoder-decoder-based method well suited for the polyp with varying shape, size, and scales. Second, we utilize the teacher-student concept of training the model, where the teacher model is the student model's exponential average. Third, to leverage the unlabeled dataset, we enforce a consistency technique and force the teacher model to generate a similar output on the different perturbed versions of the given input. Finally, we propose a method that upgrades the traditional pseudo-label method by learning the model with continuous update of pseudo-label. We show the efficacy of our proposed method on different polyp datasets, and hence attaining better results in semi-supervised settings. Extensive experiments demonstrate that our proposed method can propagate the unlabeled dataset's essential information to improve performance.

Dual optical coherence tomography sensor guided, two-motor, horizontal SMART micro-scissors.

In microsurgery, the physiological hand tremor of the surgeon remains an important factor affecting procedure efficiency, risk of complications, and ultimately, the efficacy of treatment. The micro-scissors are routinely employed to perform precise sharp dissection of delicate tissues. Here, we present a dual optical coherence tomography (OCT) distance sensor guided, two-motor, horizontal smart micromanipulation aided robotic-surgery tool (SMART) micro-scissors. It is intended to improve surgeon performance by retaining all of the attributes of the horizontal scissors while implementing proof-of-concept use of two functional motors to provide tremor cancellation.

Development and preliminary results of bimanual smart micro-surgical system using a ball-lens coupled OCT distance sensor.

Bimanual surgery enhances surgical effectiveness and is required to successfully accomplish complex microsurgical tasks. The essential advantage is the ability to simultaneously grasp tissue with one hand to provide counter traction or exposure, while dissecting with the other. Towards enhancing the precision and safety of bimanual microsurgery we present a bimanual SMART micro-surgical system for a preliminary ex-vivo study. To the best of our knowledge, this is the first demonstration of a handheld bimanual microsurgical system. The essential components include a ball-lens coupled common-path swept source optical coherence tomography sensor. This system effectively suppresses asynchronous hand tremor using two PZT motors in feedback control loop and efficiently assists ambidextrous tasks. It allows precise bimanual dissection of biological tissues with a reduction in operating time as compared to the same tasks performed with conventional one-handed approaches.

Avian embryo monitoring during incubation using multi-channel diffuse speckle contrast analysis.

Determining the survival rate of avian embryos during incubation is essential for cost-saving in the poultry industry. A multi-channel diffuse speckle contrast analysis (DSCA) system, comprising four optical fiber channels, is proposed to achieve noninvasive in vivo measurements of deep tissue flow. The system was able to monitor chick embryo vital signs over the entire incubation period. Moreover, it proved useful in distinguishing between chick embryos in healthy and weakened conditions.

Insect transferrin functions as an antioxidant protein in a beetle larva.

In insects transferrin is known as an iron transporter, an antibiotic agent, a vitellogenin, and a juvenile hormone regulated protein. Here, a novel functional role for insect transferrin as an antioxidant protein is demonstrated. Stressors, such as heat shock, fungal challenge, and H(2)O(2) exposure, cause upregulation of the white-spotted flower chafer Protaetia brevitarsis (Coleoptera: Scarabaeidae) transferrin (PbTf) mRNA in the fat body and increases PbTf protein levels in the hemolymph. RNA interference (RNAi) treated PbTf reduction causes increased iron and H(2)O(2) levels in the hemolymph and results in induction of apoptotic cell death in the fat body during exposure to stress. The observed effects of PbTf RNAi suggest that PbTf inhibits stress-induced apoptosis by diminishing the Fenton reaction via the binding of iron, thus supporting an antioxidant role for PbTf in stress responses.