Interdisciplinary – Page 47 – UROP Spring Symposium 2021

Interdisciplinary

Role of the MYRF transcription factor in retinal development

Vision is critical to quality of life. Diseases of the retina, which senses light and sends signals to the brain, can lead to significant vision impairment. One such disease called nanophthalmos affects the overall growth of the eye and can be associated with genetic mutations in the myelin regulatory factor (MYRF) gene. In this project, we use a mouse model to examine the role of the MYRF as a regulator of eye development and disease in the RPE, and to define new genes that could be implicated in retinal disease. Myrf is a transcription factor expressed in the retinal pigment epithelium (RPE), a supporting cell in the retina. We utilized single cell RNA sequencing, scRNA, from mouse eye cups, at varying stages of development to compare gene expression profiles with wild-type eye cups to those loss of Myrf. We used Seurat software for analysis to identify genes that are differently expressed in these two genotypes among different cell types to provide clues into how MYRF impairs eve development. We hypothesize that genes involved in pigmentation and structure of the RPE will be most affected by loss of MYRF, given that mice lacking MYRF have pigmentary defects and develop retina degeneration. To identify which of the genes regulated by MYRF could be implicated in retinal disease, we have also used Clinvar, an online database to search for diseases associated with genes and their allele variants. We have found approximately seventy-seven genes that are likely regulated by MYRF, but not yet linked to human disease, which will now be screened for variants in disease populations. This project will further our understanding of eye disease caused by MYRF mutations, discover genes regulated by mouse Myrf in eye development, and may lead to new targets for treatments.

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Measurement of Coronary Artery Diameter using Image Processing and Geometric Modeling

While doctors use their eyes to understand and interpret coronary artery data provided by Magnetic Resonance Angiography (MRA) or Computed Tomography Angiography (CTA), researchers have been looking for ways to use technology to automatically track coronary arteries. This document looks to combine two methods of interpreting angiograms, Kalman filtering and a geometric vessel model in order to glean a fuller estimate of a coronary artery’s radius. So far, I have created an algorithm in MATLAB to detect radii across different artery connections. The next step is to compare these radii results to formulae referenced in other research papers and apply the two aforementioned methods to improve the accuracy of my radii measurements. A better estimate of a coronary artery’s radius will prove vital to doctors, who, in relying on their eyes to interpret coronary artery data, might struggle to differentiate noise from weak stenosis requiring treatment. Further, these doctors, though they differ from each other on how they treat varying levels of stenosis, would love to have a standardized way of understanding how severe the stenosis is, and having a more reliable radius will provide exactly that.

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Developing fast and unbiased computer vision algorithms

Computer Vision Algorithms are a fast-developing technology, and we have seen from example that they are currently not as accurate and unbiased as we hope they can be. Our project aims to develop a more efficient system and algorithm to reduce bias in computer vision programs. One way bias may be introduced into these algorithms is through issues with light levels in images and videos. Since many computer vision algorithms rely on video cameras, as opposed to infrared or another type of light, the lack of light in videos introduces uncertainty in a program, which can produce bias, where some categories of images are more accurately processed by the algorithm than others. This bias can manifest itself in different scenarios, such as during nighttime or when recording people with darker skin, and these are the biases that we aim to correct. My part in the project involved labelling the videos that are going to be used for analysis for the algorithms, and attempting to help create a standardized method of labelling in order to have a set of videos with which the algorithm can be trained with. Our sample set was purposefully selected to have a variety of videos with different light levels and skin tones. Our ultimate purpose was to label as many videos as possible to use later on in the project, where other groups are working on developing the algorithm and all other overarching parts of the project. The main project was not completed, and likely will not for some years, but we achieved our loose goal of labelling videos.

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Identifying Brain Edema in CT Scans Using Machine Learning

Brain edema is the swelling of the brain as a result of traumatic brain injuries, strokes, tumors, and infections. This affects the patients cognitive and motor function and can lead to lasting adverse health risks and death. Early and accurate identification of edema can prevent these hazards. Studies have found that brain edema is difficult for clinicians to accurately identify, as it often blends in with other brain matter. Additionally finding a link between the volume of edema and the effect on the patient is considered valuable, but there is currently no standard software in place for this end. Even when clinicians are able to identify edema, they are not able to quantify the volume present. Convolutional neural networks were used for training the model to segment the edema region. Images from the PROTECT III collection at the University of Michigan hospital were used for this research. Some images were previously annotated by clinicians and these images were subsequently used in the process of training the machine learning model. The performance of the model was evaluated using quantitative techniques such as dice, sensitivity, specificity, accuracy, and AUC. The goal of this software is to decrease adverse effects and death related to brain edema by creating a system to quantitatively measure edema and make informed decisions on how to treat the patient based on the information collected.

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Developing fast and unbiased computer vision algorithms

In an effort to improve driver safety and autonomous vehicle testing, video recordings of drivers allow for data to be analyzed. These videos are first examined by human coders, but a more efficient, automated algorithm would prevent the need for human coders entirely. However, in order to build the algorithm, human coders need to analyze videos of drivers and label various actions, such as if the driver is turning or tilting their head, or hand movements, such as texting, and if their hand is obscured. Once these labels are implemented, they are tested against each other for accuracy, so that the final algorithm is unbiased enough to be implemented into vehicle safety.

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Head CT image analysis for detecting edema

Cerebral edema, which is swelling in the brain, is commonly found in patients suffering from head trauma, injuries, or other diseases and can be fatal. The purpose of this research project was to develop a method for automatically detecting and segmenting edema in head CT scans in order to make it faster and easier for clinicians to diagnose and treat traumatic brain injury (TBI) patients. However, edema is difficult to segment due to its unclear boundaries and its similarity in pixel value to other brain tissue. In previous research, most methods for segmenting edema have either been semi-automated or for MRI scans. More accurate methods require MRI scans, but even though an MRI scan is more detailed and can make it easier to segment edema, CT scan is the gold standard for evaluating brain injuries and is faster and more widely available. Therefore, automatic segmentation of CT scans will be very beneficial. In this project, the active contours without edges method, developed by Chan and Vese, is used with manually segmented hematoma as the initial contour. The method was developed in MATLAB. The segmented edema was then compared with the manually segmented images, and the DICE score was used to measure the accuracy. Currently, this method successfully segments select CT scans. However, it needs improvement in order to be more generalizable. In the future, I will look further into other techniques such as deep learning that can help improve accuracy and generalizability in automatic edema segmentation.

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Developing Fast and Unbiased Computer Vision Algorithms

The research project I am participating in is “Developing Fast and Unbiased Computer Vision Algorithms” through the Multidisciplinary Design Program and the Transportation Research Institute. We’re trying to make a computer vision algorithm that could detect if drivers are paying attention to the road or distracted such as being on their phones. The algorithm itself should be as efficient and reliable as possible. To get our results, we look at frames of videos of people driving and create data sets and coding logs based on what the driver is doing. We have multiple people log the videos to create a benchmark of what the driver is doing. We also change our operational definitions of what we are looking for in the videos. The coding logs give us a benchmark for the algorithm so it can accurately judge what actions are distracted driving. By changing the variables we’re analyzing and improving the benchmark, we can make the algorithm more efficient, especially when we have a lot of different types of videos with varying lighting, subjects, and difficulty. Our research is vital because while transportation safety is important and an accurate algorithm detection distracted driving could help reduce the number of car accidents and car deaths, on a larger scale, our improvements of this computer vision algorithm would help improve how computer vision algorithms are created and applied in general. Overall, computer vision algorithms have shown to be biased especially with variables like skin color, sex/gender expression, and lighting. Those variables negatively affect the accuracy of the algorithm. Through our research, we could use our same methods and data to help other computer vision algorithms become more accurate and efficient.

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Pancreas Segmentation via Image Processing and Machine Learning

Pancreas segmentation is consistently one of the least accurate out of all organ segmentation due to its low contrast in its boundary and high anatomical variability in its geometric properties. Many hospitals and biomedical laboratories therefore do not have accurate methodologies for cancer detection, 3D modeling, etcetera for pancreas scans. This paper serves to improve current pancreas segmentation methods by making use of a type of Convolutional Neural Network (CNN) called a U-Net in addition to image processing techniques such as an Integrated Hausdorff-Sine Loss Function for preprocessing and 3D Gaussian smoothing for post processing. The CNN was trained on Computed Tomography (CT) images in n-fold cross-validation from a public NIH dataset and a private dataset from Michigan Medicine. Data augmentation was performed on the CT scans using Keras, an open-source neural network library. Accuracy and precision of the CNN was tested using a Dice-Sørensen Similarity Coefficient (DSC) and Jaccard Index (JI). The mean DSC and JI achieved for the proposed methods are expected to be at least 70.00%.

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Study of Substance Cravings Versus Ordinary Urges

While there are many studies of cravings and addiction to substances such as cigarettes, it is difficult to pinpoint one universal craving scale that is used to compare all the results on an even plane and to compare them against an “everyday” craving for something such as junk food. This study creates a standard craving scale that can be used to compare the cravings that one has for cigarettes and that one has for junk food. In this experiment, prior smokers in an online smoking quitters forum were surveyed using a questionnaire style scale adapted from the scale used for trichotillomania ,a compulsive hair picking disorder used in a prior study, the Fagerstrom Test for Nicotine Dependence, the Yale Cravings Study, the Questionnaire of Smoking Urges, the Cigarette Dependence Scale, the Fagerstrom Tolerance Questionnaire, the Minnesota Nicotine Withdrawal Scale, and the Wisconsin Smoking Withdrawal Scale. This exact scale with in depth questions about strong cravings was used to evaluate the same subject pool but instead describing their strong cravings for junk food. The means and standard deviations for both types of questions can be compared with the same exact scale, and we expect that the cravings for unhealthy junk food will be similar in ranking. We expect that the ratings on the craving scale will be around middle range. This study can be further implicated in various research studying cravings and addiction, and it can bring attention to the need for a universal craving scale to compare data across studies. This research can also prompt further research on cravings and how they are not the only driving factor when it comes to addiction. These findings can be applied to further discussions about the ability to control “everyday” cravings and what makes them socially acceptable or non-addictive.

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Study of Substance Cravings versus Ordinary Urges

Smoking addiction is one of the most widely studied phenomena, yet the characteristics of urges to smoke are not widely understood. This research project focused on comparing the severity of smoking urges to the severity of everyday cravings such as the urge to eat unhealthy foods. As these everyday cravings are experienced by most people, it offered a great mode of comparison to gain a more nuanced understanding of smoking cravings. We found participants on an online forum who experienced cigarette cravings and participants who experienced unhealthy food cravings to report their urges on a variety of different scales indicating the intensity of craving, the frequency of their cravings, and more. In our preliminary research, we found that cigarette craving strength means across a variety of studies was only a little over .5, which was not as high as we had originally predicted that urges to smoke would be. We hypothesize that results will show that the strength of smoking cravings are actually more similar to everyday cravings than one might think. These results may help clinicians understand the smoking addiction and the intensity of smoking cravings and help with treatment strategies.

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