Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299245
Başak Lara Günal, V. Keskin, F. Kartufan, ve Özge Köner
Zoonotic retroviruses can cause widespread morbidity and mortality. Preventive vaccines are currently available for a limited number of viruses. Since an effective vaccine against COVID19 cannot be developed yet, personal protection equipment (PPE) is essential, especially for protecting the healthcare providers against such contaminations. Full face protecting equipment has a vital role in PPE. During the April 2020 spreading period of the COVID-19 epidemic, filter adapters were required to create a snorkel based full face mask as a PPE. This study aimed to report different production methods for filter adapters, features, advantages-disadvantages and combining the resulting mask’s physical characteristics and cost analysis.
{"title":"Development of a Full Face Mask during the COVID-19 Epidemic Spread Period","authors":"Başak Lara Günal, V. Keskin, F. Kartufan, ve Özge Köner","doi":"10.1109/TIPTEKNO50054.2020.9299245","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299245","url":null,"abstract":"Zoonotic retroviruses can cause widespread morbidity and mortality. Preventive vaccines are currently available for a limited number of viruses. Since an effective vaccine against COVID19 cannot be developed yet, personal protection equipment (PPE) is essential, especially for protecting the healthcare providers against such contaminations. Full face protecting equipment has a vital role in PPE. During the April 2020 spreading period of the COVID-19 epidemic, filter adapters were required to create a snorkel based full face mask as a PPE. This study aimed to report different production methods for filter adapters, features, advantages-disadvantages and combining the resulting mask’s physical characteristics and cost analysis.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114547734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299243
F. Nassehi, Başak Erdoğdu, Sena Şişman, Yağmur Sağlam, O. Eroğul
Topic of self-driving mode and transition to this mode is one of the trend topics of biomedical engineering and artificial intelligence studies. Sleeplessness and sleep efficiency to cause inattention in driving and accidents. This study aimed to investigate convenient time to transit self-driving mode respect to number of accidents and sleep efficiency of driver by using Support Vector Machines and K-Nearest neighbors classification algorithms to reduce the accidents. Approximate entropy and Lyapunov exponent for Electroencephalography and dominant frequency, ratio of power of high frequency to low frequency, area under the curve and derivative respiration signals were extracted. This proposal method achieves 93.33% and 100% accuracies to classify drivers and transit car to self-driving mode respect to two criteria.
{"title":"A Study On Finding The Optimal Time For Automatic Transition To Self-Driving Mode","authors":"F. Nassehi, Başak Erdoğdu, Sena Şişman, Yağmur Sağlam, O. Eroğul","doi":"10.1109/TIPTEKNO50054.2020.9299243","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299243","url":null,"abstract":"Topic of self-driving mode and transition to this mode is one of the trend topics of biomedical engineering and artificial intelligence studies. Sleeplessness and sleep efficiency to cause inattention in driving and accidents. This study aimed to investigate convenient time to transit self-driving mode respect to number of accidents and sleep efficiency of driver by using Support Vector Machines and K-Nearest neighbors classification algorithms to reduce the accidents. Approximate entropy and Lyapunov exponent for Electroencephalography and dominant frequency, ratio of power of high frequency to low frequency, area under the curve and derivative respiration signals were extracted. This proposal method achieves 93.33% and 100% accuracies to classify drivers and transit car to self-driving mode respect to two criteria.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129228541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299302
Ş. K. Özen, M. Aksahin
For the segmentation of brain vessels from MRA images, brain tissue is used in the head, eye, skull, etc. must be separated from the structures. For this reason, studies are carried out for the segmentation of brain tissue. In this study, the method that automatically segregates brain tissue from magnetic resonance angiography images taken with time of flight (TOF) technique is presented. The method in the study consists of five steps. First of all, the tip contrast values in the image are filtered by anisotropic diffusion filtering method. Parameters of anisotropic diffusion method are determined automatically by the natural image quality evaluator method. Sudden density transitions are detected by applying LoG edge detection filter on the filtered image. It is made ready for image analysis by applying etching on the image with density transitions. According to the conditions determined in image analysis, brain tissue is obtained separated from other head structures. As a result of this study, an easy-to-apply, fast-delivering, high-accuracy automatic algorithm has been introduced.
{"title":"Automatic Brain Tissue Segmentation on TOF MRA Image","authors":"Ş. K. Özen, M. Aksahin","doi":"10.1109/TIPTEKNO50054.2020.9299302","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299302","url":null,"abstract":"For the segmentation of brain vessels from MRA images, brain tissue is used in the head, eye, skull, etc. must be separated from the structures. For this reason, studies are carried out for the segmentation of brain tissue. In this study, the method that automatically segregates brain tissue from magnetic resonance angiography images taken with time of flight (TOF) technique is presented. The method in the study consists of five steps. First of all, the tip contrast values in the image are filtered by anisotropic diffusion filtering method. Parameters of anisotropic diffusion method are determined automatically by the natural image quality evaluator method. Sudden density transitions are detected by applying LoG edge detection filter on the filtered image. It is made ready for image analysis by applying etching on the image with density transitions. According to the conditions determined in image analysis, brain tissue is obtained separated from other head structures. As a result of this study, an easy-to-apply, fast-delivering, high-accuracy automatic algorithm has been introduced.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299289
A. Narin
Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.
{"title":"Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images","authors":"A. Narin","doi":"10.1109/TIPTEKNO50054.2020.9299289","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299289","url":null,"abstract":"Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123684102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299312
Oyku Sarigil, Muge Anil-Inevi, Esra Yılmaz, Ozge S Ozcelik, Gulistan Mese, H. Tekin, E. Ozcivici
Magnetic levitation is a promising technique for tissue engineering with contact- and label-free approach. Levitation-based biofabrication systems emerge as a simple, rapid and versatile alternative to traditional tissue culture systems, since biofabrication specs can easily be tailored via magnet shape and configuration. This study aims at possible magnetic levitation systems for culture of adipose tissue cells. In this study, we performed two different magnet configurations, vertical and horizontal deployment, in an effort to be utilized in adipose tissue engineering.
{"title":"Magnetic levitation-based adipose tissue engineering using horizontal magnet deployment","authors":"Oyku Sarigil, Muge Anil-Inevi, Esra Yılmaz, Ozge S Ozcelik, Gulistan Mese, H. Tekin, E. Ozcivici","doi":"10.1109/TIPTEKNO50054.2020.9299312","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299312","url":null,"abstract":"Magnetic levitation is a promising technique for tissue engineering with contact- and label-free approach. Levitation-based biofabrication systems emerge as a simple, rapid and versatile alternative to traditional tissue culture systems, since biofabrication specs can easily be tailored via magnet shape and configuration. This study aims at possible magnetic levitation systems for culture of adipose tissue cells. In this study, we performed two different magnet configurations, vertical and horizontal deployment, in an effort to be utilized in adipose tissue engineering.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124895882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299320
Ç. Erdaş, Didem Ölçer
One of the diseases with high prevalence among the consequences of cardiovascular diseases is heart failure. Heart failure is a condition in which the muscles in the heart wall become faded and dilated, limiting the heart’s ability to pump blood. As time passes, the heart cannot meet the proper blood requirement in the body, and as a result, the person has difficulty breathing. As the human age increases, the incidence of heart failure gradually increases, and the rate of mortality due to heart failure also increases. In this context, close monitoring of people suffering from this disease will significantly increase the survival rate. In this study, a machine learning-based system is proposed to predict the mortality-survival status of patients with heart failure. Thus, by identifying people with mortality risk, the survival probability of the patients may increase with more effective and close follow-up.
{"title":"A Machine Learning-Based Approach to Detect Survival of Heart Failure Patients","authors":"Ç. Erdaş, Didem Ölçer","doi":"10.1109/TIPTEKNO50054.2020.9299320","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299320","url":null,"abstract":"One of the diseases with high prevalence among the consequences of cardiovascular diseases is heart failure. Heart failure is a condition in which the muscles in the heart wall become faded and dilated, limiting the heart’s ability to pump blood. As time passes, the heart cannot meet the proper blood requirement in the body, and as a result, the person has difficulty breathing. As the human age increases, the incidence of heart failure gradually increases, and the rate of mortality due to heart failure also increases. In this context, close monitoring of people suffering from this disease will significantly increase the survival rate. In this study, a machine learning-based system is proposed to predict the mortality-survival status of patients with heart failure. Thus, by identifying people with mortality risk, the survival probability of the patients may increase with more effective and close follow-up.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/tiptekno50054.2020.9299247
{"title":"TIPTEKNO 2020 Index","authors":"","doi":"10.1109/tiptekno50054.2020.9299247","DOIUrl":"https://doi.org/10.1109/tiptekno50054.2020.9299247","url":null,"abstract":"","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114741967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299274
M. B. Terzi, V. Arikan
In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-the-art signal processing and machine learning methods is developed to perform the robust detection of cardiac arrhythmia (CA). For this purpose, a signal processing technique that simultaneously obtains SKNA and ECG from wideband recordings on MIT-BIH database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of CA is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform the robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform the robust detection of outliers that correspond to CA. The performance results of the proposed technique over MIT-BIH database showed that the technique provides highly reliable detection of CA by performing the robust detection of SKNA anomalies. Therefore, in cases where the diagnostic information of ECG is not sufficient for the reliable diagnosis of CA, the proposed technique can provide early diagnosis of the disease, which can lead to a significant reduction in the mortality rates of cardiovascular diseases.
{"title":"Detection of Cardiac Arrhythmia using Autonomic Nervous System, Gaussian Mixture Model and Artificial Neural Network","authors":"M. B. Terzi, V. Arikan","doi":"10.1109/TIPTEKNO50054.2020.9299274","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299274","url":null,"abstract":"In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-the-art signal processing and machine learning methods is developed to perform the robust detection of cardiac arrhythmia (CA). For this purpose, a signal processing technique that simultaneously obtains SKNA and ECG from wideband recordings on MIT-BIH database is developed. By using preprocessed data, a novel feature extraction technique which obtains SKNA features that are critical for the reliable detection of CA is developed. By using extracted features, a supervised learning technique based on artificial neural network (ANN) and an unsupervised learning technique based on Gaussian mixture model (GMM) are developed to perform the robust detection of SKNA anomalies. A Neyman-Pearson type of approach is developed to perform the robust detection of outliers that correspond to CA. The performance results of the proposed technique over MIT-BIH database showed that the technique provides highly reliable detection of CA by performing the robust detection of SKNA anomalies. Therefore, in cases where the diagnostic information of ECG is not sufficient for the reliable diagnosis of CA, the proposed technique can provide early diagnosis of the disease, which can lead to a significant reduction in the mortality rates of cardiovascular diseases.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122553188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299303
Ziyşan Buse Yarali Çevіk, Başak Olcay, O. Karaman
Breast cancer is a fatal disease, and it is one of the most common cancer types among women in the world. The traditional monolayer methods are used to treat diseases. However, the method is limited in terms of the cell to cell communication and responses of cells to drugs. One of the main goals of cancer treatments is to prevent tumor metastasis and prevent diffusion in various areas of the body, thereby it is needed to increase the effectiveness of the treatments and reduce side effects. Peptides can be used in cancer treatment. Most peptide studies are performed in monolayer culture. These cultures can not accurately represent the complex intercellular and intracellular environment in clinical studies. Peptide studies must be performed in scaffold-free conditions to mimic the natural responses of cells. The study has been performed as scaffold-free microtissues with different NGR peptide concentrations. Results have been evaluated in terms of diameters, and viability of microtissues. It is concluded that 2 mM NGR is the most effective concentration in MCF-7 microtissue treatment.
乳腺癌是一种致命的疾病，是世界上女性中最常见的癌症类型之一。传统的单层方法用于治疗疾病。然而，该方法在细胞间通讯和细胞对药物的反应方面受到限制。癌症治疗的主要目标之一是防止肿瘤转移和扩散到身体的各个部位，因此需要提高治疗的有效性和减少副作用。多肽可用于癌症治疗。大多数肽研究都是在单层培养中进行的。在临床研究中，这些培养不能准确地代表复杂的细胞间和细胞内环境。肽研究必须在无支架的条件下进行，以模拟细胞的自然反应。该研究是在不同NGR肽浓度的无支架微组织中进行的。结果已经在直径方面进行了评估，以及微组织的活力。结果表明，2 mM NGR是MCF-7微组织处理的最有效浓度。
{"title":"Determination of Optimum Concentration of NGR Peptide With Anticancer Effect On Breast Cancer Microtissue","authors":"Ziyşan Buse Yarali Çevіk, Başak Olcay, O. Karaman","doi":"10.1109/TIPTEKNO50054.2020.9299303","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299303","url":null,"abstract":"Breast cancer is a fatal disease, and it is one of the most common cancer types among women in the world. The traditional monolayer methods are used to treat diseases. However, the method is limited in terms of the cell to cell communication and responses of cells to drugs. One of the main goals of cancer treatments is to prevent tumor metastasis and prevent diffusion in various areas of the body, thereby it is needed to increase the effectiveness of the treatments and reduce side effects. Peptides can be used in cancer treatment. Most peptide studies are performed in monolayer culture. These cultures can not accurately represent the complex intercellular and intracellular environment in clinical studies. Peptide studies must be performed in scaffold-free conditions to mimic the natural responses of cells. The study has been performed as scaffold-free microtissues with different NGR peptide concentrations. Results have been evaluated in terms of diameters, and viability of microtissues. It is concluded that 2 mM NGR is the most effective concentration in MCF-7 microtissue treatment.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121145882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299319
Emre Kayan, ve Tarık Kavuşan, Sevgi Önal, D. P. Okvur, ve Özden Y. Özuysal, B. U. Töreyin, D. Ünay
Analyses of morphology, polarity, and motility of cells is important for cell biology research such as metastatic and invasive capacity of cells, wound healing, and embryonic development. Automation of such analyses using image series of phase-contrast optical microscopy, which allows label-free imaging of live cells in their living environment, is a need. With this purpose, in this study image series of a cell motility experiment is manually annotated, and an automation algorithm realizing motion and shape analyses of cells using the annotated data is developed. In addition, due to the low number of annotated data at hand, a U-Net based solution is devised for automated segmentation of the cells and its performance is evaluated.
{"title":"A Preliminary Study on Cell Motility Analysis from Phase-Contrast Microscopy Image Series","authors":"Emre Kayan, ve Tarık Kavuşan, Sevgi Önal, D. P. Okvur, ve Özden Y. Özuysal, B. U. Töreyin, D. Ünay","doi":"10.1109/TIPTEKNO50054.2020.9299319","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299319","url":null,"abstract":"Analyses of morphology, polarity, and motility of cells is important for cell biology research such as metastatic and invasive capacity of cells, wound healing, and embryonic development. Automation of such analyses using image series of phase-contrast optical microscopy, which allows label-free imaging of live cells in their living environment, is a need. With this purpose, in this study image series of a cell motility experiment is manually annotated, and an automation algorithm realizing motion and shape analyses of cells using the annotated data is developed. In addition, due to the low number of annotated data at hand, a U-Net based solution is devised for automated segmentation of the cells and its performance is evaluated.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117208591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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