Convolutional Neural Networks Cascade for Automatic Pupil and Iris Detection in Ocular Proton Therapy

The emerging role of Artificial Intelligence in proton therapy: A review

Artificial intelligence (AI) has made a tremendous impact in the space of healthcare, and proton therapy is not an exception. Proton therapy has witnessed growing popularity in oncology over recent decades, and researchers are increasingly looking to develop AI and machine learning tools to aid in various steps of the treatment planning and delivery processes. This review delves into the emergent role of AI in proton therapy, evaluating its development, advantages, intended clinical contexts, and areas of application. Through the analysis of 76 studies, we aim to underscore the importance of AI applications in advancing proton therapy and to highlight their prospective influence on clinical practices.

Trial frame for evaluating eye movements

Eye movement research is an established methodology and widely used in fundamental research fields such as psychology or neuroscience. Most research is performed in controlled environments with screen-based eye trackers, but demand for a wearable requirement is on the rise. Yet, a state of the art wearable eye tracking system thus far has not been feasible for subjects with refractive errors; therefore, a trial frame is proposed for researching the eye movement of people with or without refractive errors. Two iris cameras and several infrared radiation LEDs were fixed behind the frame for recording eye images. The images were sent to a host computer using 2.4G wireless, which was also fixed on the frame. The entire electronic system in the trial frame was driven using a battery. Eye position in the images was recognized and segmented by a model that was trained through machine learning. Then the pupil and blinks were detected from the segmented eye area using ellipse fitting and the eye aspect ratio. Experiments showed that the correct rate of pupil and blink detections were 97.24% and 96.14%, respectively.

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Abstract

The containment liner plate (CLP) is a thin layer of carbon steel material applied as a base for concrete structures protecting nuclear material. The structural health monitoring of the CLP is critical to ensure the safety of nuclear power plants. Hidden defects in the CLP can be identified utilizing ultrasonic tomographic imaging techniques such as the reconstruction algorithm for the probabilistic inspection of damage (RAPID) methodology. However, Lamb waves have a multimodal dispersion feature, which makes the selection of a single mode more difficult. Thus, sensitivity analysis was utilized since it allows for the determination of each mode's level of sensitivity as a function of frequency; the S0 mode was chosen after examining the sensitivity. Even though proper Lamb wave mode was selected, the tomographic image had blurred zones. Blurring reduces the precision of an ultrasonic image and makes it more difficult to distinguish the dimensions of the flaw. To enhance the tomographic image of the CLP, deep learning architecture such as U-Net was utilized for the segmentation of the experimental ultrasonic tomographic image, which includes an encoder and decoder part for better visualization of the tomographic image. Nevertheless, collecting enough ultrasonic images to train the U-Net model was not economically feasible, and only a small number of the CLP specimens can be tested. Thus, it was necessary to utilize transfer learning and get the values of the parameters from a pre-trained model with a much larger dataset as a starting point for a new task, rather than training a new model from scratch. Through these deep learning approaches, we were able to eliminate the blurred section of the ultrasonic tomography, leading to images with clear edges of defects and no blurred zones.

Graphical abstract

Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data

The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R2) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations.

Non-Cancer Effects following Ionizing Irradiation Involving the Eye and Orbit

Simple Summary

The irradiation of tumors involving the eye or orbit represents a complex therapeutic challenge due to the proximity between the tumor and organs that are susceptible to radiation. The challenges include tumor control, as it is often a surrogate for survival; organ (usually the eyeball) preservation; and the minimization of damage of sensitive tissues surrounding the tumor in order to preserve vision. Anticipation of the spectrum and severity of radiation-induced complications is crucial to the decision of which technique to use for a given tumor. The aim of the present review is to report the non-cancer effects that may occur following ionizing irradiation involving the eye and orbit and their specific patterns of toxicity for a given radiotherapy modality. The pros and cons of conventional and advanced forms of radiation techniques and their clinical implementation are provided with a clinical perspective.

Abstract

The eye is an exemplarily challenging organ to treat when considering ocular tumors. It is at the crossroads of several major aims in oncology: tumor control, organ preservation, and functional outcomes including vision and quality of life. The proximity between the tumor and organs that are susceptible to radiation damage explain these challenges. Given a high enough dose of radiation, virtually any cancer will be destroyed with radiotherapy. Yet, the doses inevitably absorbed by normal tissues may lead to complications, the likelihood of which increases with the radiation dose and volume of normal tissues irradiated. Precision radiotherapy allows personalized decision-making algorithms based on patient and tumor characteristics by exploiting the full knowledge of the physics, radiobiology, and the modifications made to the radiotherapy equipment to adapt to the various ocular tumors. Anticipation of the spectrum and severity of radiation-induced complications is crucial to the decision of which technique to use for a given tumor. Radiation can damage the lacrimal gland, eyelashes/eyelids, cornea, lens, macula/retina, optic nerves and chiasma, each having specific dose–response characteristics. The present review is a report of non-cancer effects that may occur following ionizing irradiation involving the eye and orbit and their specific patterns of toxicity for a given radiotherapy modality.

Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study

Background-The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor 'cleanliness' was analysed among other factors. Method-Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results-The results show an inspection accuracy of 86.8% and 66.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions-Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.