FBE- Biyomedikal Mühendisliği Lisansüstü Programı
Bu topluluk için Kalıcı Uri
Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı altında bir lisansüstü programı olup, sadece yüksek lisans düzeyinde eğitim vermektedir.
Bu programın amaçları :
Biyomedikal Mühendisliği alanında, hastalık ve engellerin tanı ve sağaltımında, evrensel bilimin ve eğitimin kültür ve değerlerini temel alan, uluslararası düzeyde nitelikli bilgi ve teknoloji üretebilecek düzeye gelmektir.
Gözat
Sustainable Development Goal "Goal 3: Good Health and Well-being" ile FBE- Biyomedikal Mühendisliği Lisansüstü Programı'a göz atma
Sayfa başına sonuç
Sıralama Seçenekleri
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ÖgeFully supervised and semi-supervised semantic segmentation of cardiac MR using deep learning( 2021-09-02) Bolhassani, Mahyar ; Öksüz, İlkay ; 504191413 ; Biomedical Engineering ; Biyomedikal MühendisliğiHeart diseases are one of the primary causes of death worldwide. A key factor to accurately and effectively treating heart diseases is to have quantified measures like high-quality images of the organ. When we provided physicians with medical scans, they can pinpoint the kind of abnormality in the heart. Cardiac Ultrasound, CT, and MRI scans are some of the modalities that we can leverage, while each modality has both advantages and disadvantages. Depending on the situation and the patients' condition, we can choose a preferred modality. We concentrate on cardiac MRI, which is a non-ionizing modality that constructs high-quality images. Segmentation of different heart areas in CMR scans such as myocardium mass, wall thickness, left ventricle (LV), right ventricle volume, and ejection fraction (EF) is a quantitative measure that assists cardiologists in diagnosing cardiac failures. Thanks to computer-aided detection (CAD) advancements, the automatic segmentation of the heart cavity for diagnosis purposes alleviates the burden of quantitative interpretation of large numbers of cardiac scans for cardiologists. The ultimate goal of training an automatic model is to predict correctly on unseen data. Therefore, we need a large number of labeled data which is a tedious and expensive task. However, the variation of CMR data acquisition from different centers or vendors demands us to have training data from almost all centers and vendors for a robust model, which is almost impossible. To address this issue, this thesis proposes a semi-supervised segmentation setup to leverage unlabeled data to segment the left ventricle, right ventricle, and myocardium regions. We utilize an enhanced version of residual U-Net architecture on a large-scale cardiac MRI dataset. Handling the class imbalanced data issue using dice loss, the improved supervised model can achieve better dice scores than a vanilla U-Net model. We applied standard augmentation techniques as well as histogram matching techniques to increase the performance of our model in the multi-domain setup. Also, we introduce a simple but efficient semi-supervised segmentation method to improve segmentation results without the need for extensive labeled data. Finally, we applied our approach to two benchmark datasets, STACOM LVQuan 18 and M\&Ms 2020 challenges, to show the potency of the proposed model. The quantitative results demonstrate the effectiveness of our proposed model. The model achieves average dice scores of 0.926, 0.933, and 0.892 for the left ventricle, right ventricle, and myocardium respectively.
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ÖgeGevşeme temelli kenar belirleme algoritması(Fen Bilimleri Enstitüsü, 1998-02-11) Güngör, Güray ; Ölmez, Tamer ; 075205 ; Biyomedikal MühendisliğiBu çalışmada esas olarak, görüntü işleme konusunda temel öneme sahip kenar belirleme işlemlerine yer verilmiştir. Ayrıca görüntü iyileştirme konusuna da değinilmektedir. Ele alınan görüntülerdeki bozucu etkiler ve bu etkilerin giderilmesi için kullanılabilecek çeşitli teknikler, ilk olarak anlatılmıştır. Daha sonra çeşitli kenar belirleme algoritmalarına değinilmiştir. Bu tezin ana konusunu oluşturan gevşeme düşüncesi genel olarak anlatılmıştır. Sonra gevşeme düşüncesi ile kurulu metodlardan bahsedilmektedir. En son olarak da kenar belirleme amacıyla oluşturulan gevşeme düşüncesine dayalı bir metod verilmiştir. Metoddan elde edilen deneysel sonuçlar, diğer bazı kenar belirleme algoritmalarının sonuçlan ile karşılaştırılmıştır.
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ÖgeMicrowave spectroscopy based breast cancer diagnosis using support vector machines(Institute of Science and Technology, 2020-07-16) Önemli, Emre ; Akduman, İbrahim ; Abdolsaheb Yılmaz, Tuba ; 504171403 ; Biomedical Engineering ; Biyomedikal MühendisliğiInteractions of electromagnetic (EM) fields with materials relies on their intrinsic dielectric properties. Knowledge of the dielectric values of each material allows to develop electromagnetic technologies in many fields including medical technologies. There are a variety of electromagnetic medical technologies such as Microwave Imaging, Electrical Impedance Tomography and radiofrequency ablation and they promise faster, safer and low-cost applications. They rely on inherent differences among the dielectric properties of various biological tissue groups and health conditions. Hence, knowledge of the tissue dielectric properties of different biological tissues is crucial for developing EM healthcare technologies. Many works have been performed to investigate difference between dielectric properties of healthy and malignant tissues. It has been discovered that healthy and malignant tissues differ for the EM interactions because of the disperancies in their dielectrical properties. This contrast have been attributed to more water presence in malignant tumors. Breast carcinoma became one of the most researched cancer because of its high incidence and mortality rate. It is responsible for twenty three percent of new cancers and fourteen percent of cancer deaths in total. Thus, early diagnosis of the breast cancer is gaining more importance. Currently, there are some diagnostic methods such as mamography or MRI. However, they have some drawbacks such as harmful effects and low accuracy. Lately, microwave imaging (MWI) gained many interests. MWI fundamentally relies on the inherent dielectric contrast between healthy and malignant tissues. In cancer resection surgeries, determination of clear surgical margins is also possible using dielectric properties. Numerous studies were performed to expand the knowledge of the dielectric properties. However, existing dielectric datasets do not include every tissue type, frequency and temperature. Hence, more studies are needed. Open-ended coaxial probe has became the most preferred measurement method, because it is non-destructive, easy and suitable for biological materials. More dielectric data requires fast and accurate classification methods. For medical applications, most preferred one is Support Vector Machines (SVM). Being a supervised classification method, SVM is widely used because of its high classification performance on medical data. In this study, performance of SVM and infinite feature selection was investigated on the dielectric data of female rat normal breast tissues and malignant tumors in microwave frequencies. Measurements were conducted between 0.5 GHz and 6 GHz with 0.55 GHz intervals at 101 frequency points. Relative permittivity, conductivity and combination of them were tested separately. Firstly, they were tested without feature selection, raw dielectric data was also compared with normalization and logarithm of the dielectric data. Raw permittivity and combined data outperformed others resulting in 100% accuracy. Note that cross validation (CV) technique does not allow memorization of the learning model. Selecting top 100 features, the algorithm resulted in 100% accuracy with permittivity data whereas using top 50 features, it resulted in 99.23% accuracy with combined data. Using nested cross validation, features were selected as top 1 to top 100. Raw permittivity data gave more than 99% accuracy for more than sixty features. Using only one feature, 83.69% accuracy was obtained. Logarithm of the conductivity data resulted in 90.31% and 90% accuracy using one feature with linear and RBF kernels respectively. Best result of conductivity data is 98% using raw data and selecting top 70 features. With one feature, frequency of 5.505 GHz resulted in the best result. S11 response was also tested to avoid dielectric property calculation and to design narrow band devices. Note that this response indicates the energy transfer between probe and biological tissue related to tissue intrinsic electrical properties. Logarithm of the data outperformed with 93.85% accuracy using 10-fold linear SVM. Feature selection step was performed with 10-fold CV. With top 100 features, logarithm of data resulted in slightly higher performance as 91.85% accuracy with RBF kernel. With top 50 features, raw data was slightly better with 85.85% accuracy using linear SVM. Nested CV was applied to logarithm of S-parameter data. Selecting top 10 to 100, with decreasing number of features, accuracy dropped from 91.69% to 87.23% for RBF kernel and 91.38% to 87.08% for linear kernel. Besides, using top 1 to 10 features, accuracy dropped from 87.23% to 86.92% for RBF kernel and 87.08% to 83.08% for linear kernel. Best feature was corresponding to real part of S11 response at 610 MHz. The results show that dielectric measurement data can become acceptable diagnostic tool for breast cancer diagnosis. Thus, development of the EM medical technologies requires more tissue dielectric data. This study provides more dielectric data to the literature and it provides a perspective for analysing the dielectric data on the classification manner.