LEE- Biyomedikal Mühendisliği-Yüksek Lisans

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http://hdl.handle.net/11527/19219

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Bilgisayarlı mikro tomografi tarama izdüşümlerinin farklı cebirsel geri çatma yöntemleri ile analizlerinin incelenmesi

(Lisansüstü Eğitim Enstitüsü, 2022) Külüşlü, Göker ; Yıldırım, İsa ; 723191 ; Biyomedikal Mühendisliği Programı

Medikal görüntüleme yöntemleri, özellikle klinikte hastalıkların tanı ve teşhisinde, tedavi sürecinin takibinde ve hatta girişimsel operasyonlar sırasında da süreç takibi için sıklıkla kullanılmaktadır. Hastalıkların tanı ve teşhisinde hızlı, güvenilir ve girişimsel olmayan bu görüntüleme yöntemleri ile iyonize ya da iyonize olmayan ışınlar kullanılarak hastanın istenilen bölgesine ait yüksek çözünürlükte görüntüler elde edilmektedir. Uzun yıllardır kullanılan ve kullanımı giderek artan medikal görüntüleme yöntemlerinden biri olan Bilgisayarlı Tomografi(BT), incelenen bir nesnenin 3 boyutlu yapısını iyonize X-ışınları kullanarak, radyasyonun farklı bölgelerde farklı tutulumlar göstermesiyle kesitleri yeniden oluşturmak için kullanılan bir görüntü yöntemidir. Yeniden yapılandırma, nesnenin etrafındaki birçok açıdan ölçülen 2 boyutlu X-ışını projeksiyonlarından oluşturulmuştur. BT sisteminin performansı, en çok X-ışını kaynağı ve X-ışını görüntülerini elde etmek için kullanılan dedektör olmak üzere birçok faktör tarafından belirlenir. Klinikte kullanılan görüntüleme yöntemleri, özellikle biyolojik ve tıbbi amaçlar için insan vücudunun görüntülenmesinde kullanılsa da destekleyici araştırma grupları tarafından da test ve analiz cihazı olarak tercih edilmektedir. Bu kapsamda kullanılan Mikroskopik Bilgisayarlı Tomografi (Mikro-BT) cihazı sağlık bilimleri, fen ve mühendislik bilimlerinde uzun yıllardır önemli çalışmalarda yer almaktadır[6]. Micro-BT taraması ile elde edilen 2B veya 3B kesit görüntülerini oluşturan pikseller, mikro boyutta olduğundan bir malzemenin iç yapısını malzemeye hiçbir zarar vermeden 3B görüntülerinin elde edilmesine ve bu görüntüler üzerinden analizlerin yapılmasına olanak sağlamaktadır. Bu özelliklerinden dolayı da Mikro-BT, hem canlı örneklerin hem de farklı karakteristik özelliklere sahip katı veya sıvı örneklerin incelenmesinde sıklıkla tercih edilen bir yöntem olmaktadır. Mikro-BT'lerin özellikle canlılar üzerinde yumuşak doku ve kemik dokuların görüntülenmesinde ve analizlerinin yapılmasında, metal ve alaşımlarının veya kompozit malzemelerin incelenmesi gibi farklı konularda kullanım alanlarının olduğu bilinmektedir. BT görüntülerinin yeniden yapılandırılması, hastaya bir çok farklı açılarda gönderilen ve elde edilen X ışını verilerini görüntüye dönüştüren matematiksel bir işlemdir. Görüntü yeniden yapılandırılması görüntü kalitesi üzerinde ve işlem esnasında uygulanacak X-ışını dozu üzerinde temel etkiye sahiptir. Kesit görüntülerinin elde edilmesi iki temel yeniden yapılandırma yöntemi mevcuttur.
Self-supervised deep convolutional neural network training for low-dose CT reconstruction

(Graduate School, 2022) Ünal, Mehmet Ozan ; Yıldırım, İsa ; 504181414 ; Biomedical Engineering Programme

Computed tomography (CT) is a medical imaging technique to obtain a noninvasively three-dimensional image of the body. CT imaging is one of the most crucial tools which is used for monitoring the human body and diagnosing serious illnesses. In CT imaging, one of the most serious concerns has been ionizing radiation since exposure to large amounts of radiation can cause serious illnesses. Various low-dose CT reconstruction approaches have been proposed to reduce the dose level without compromising image quality. With the emergence of deep learning, the increasing availability of computational power, and huge datasets, data-driven methods have recently gotten a lot of attention. Deep learning-based methods have also been applied in various ways to address low-dose CT reconstruction problem. However, the success of these methods is usually dependent on labeled data, which requires tedious work by radiologists for CT imaging. Recent studies, however, have also shown that training may be done successfully with noisy datasets without the requirement of noise-free target data. In this study, a training scheme is defined to use low-dose projections as their own training targets. We apply the self-supervision principle in the projection domain where the noise is element-wise independent, which is a requisite for self-supervised training methods. The parameters of a denoiser neural network are optimized through self-supervised training. Experiments are done with both analytical and human CT data. Slices from deep lesion dataset for human CT data and ellipses dataset for synthetic data are used. To simulate low-dose settings, 64 views parallel beam geometry is used. The noisy projections are created with additive white Gaussian noise with 30, 33, and 37 dB SNR values. The proposed method is compared with FBP, SART, SART+TV, SART+BM3D, and the supervised FBP+U-Net method. The methods are compared quantitatively with PSNR and SSIM metrics, and the reconstructions are qualitatively assessed regarding background smoothness, the sharpness of the details, and the recoverability of the lesions with some visual examples. In the comparisons, it is shown that the proposed method outperforms both traditional and compressed sensing-based iterative reconstruction methods in the reconstruction of analytic CT phantoms and real-world CT images in the low-dose CT reconstruction task, both qualitatively and quantitatively. Besides, it produces comparable results with the supervised approach.
Machine learning based augmentation of medical microwave imaging

(Graduate School, 2022-02-07) Şafak Kaplan, Merve ; Çayören, Mehmet ; 504171406 ; Biomedical Engineering

The main reason why microwave imaging, which is an alternative to in-body imaging methods, is desired to be used in biological systems is that it is not ionized. Other reasons why it is desired to be used for the detection of udder tumor can be said that udder compression is not performed and the device materials are more affordable. Each tissue has its dielectric properties different from another tissue. However, if the tissue has the same tissue but is tumorous, it will now have different dielectric properties due to the excess blood flow in it. Quantitative methods, one of the classes of microwave inverse problems, can also show the permeability and conductivity properties of the scatterer, however, it has been stated that only position and shape information can be determined in the qualitative methods, which are stated as computationally efficient and fast in the literature. However, with the solution of qualitative inverse problems, apart from the position and shape information of the scatterer, the electromagnetic properties of the scatterer should also be determined. In addition to the well-known qualitative inverse methods in the literature, linear sampling method (LSM) and, factorization method (FM), inverse time migration (RTM) method is also used to reconstruct the images of scatterers. If the pixel changes in the images created using these methods are examined and correlated with the dielectric properties of the scatterer, this threshold of qualitative inverse problems can be eliminated. In the first part of the thesis, microwave images were created with qualitative inverse methods (LSM, FM, RTM) using s-parameters taken from a scatterer in the simulation environment. To capture the relationship between the images and the dielectric properties of the scatterer, graphs were created by calculating the maximum pixel values and averages of the location region of the object in the microwave images. When these graphs are examined, it is observed that there is a general logarithmic similarity between the pixel values in the microwave images despite the changing dielectric and position information of the scatterer. Simultaneously with this process, comparisons were made between the qualitative reverse imaging methods and the desired synthetic images by looking at the similarity of MSE values. In addition, the factors affecting the image were also observed for the scenario determined while creating the images. Thanks to the relationship between the scattering microwave images and their dielectric properties, machine learning methods were used to make classification and prediction. A dataset was created with images in different locations and dielectric properties to be used in machine learning. These images are simple microwave images and it is aimed to create more meaningful results with only the key information of the images by using the SVD method. As the machine learning algorithm used here, Adaboost, which is considered to be suitable for the data set features, was used. In the first stage, a dataset containing only microwave image singular values and dielectric permittivity values was prepared and classification was made. When the confusion matrix formed as a result of the classification was examined, it was observed that a high-accuracy classification was made. In the second stage, the possibility of predicting the singular values of the ground truth images with the singular values of the microwave images was examined. The data set at this stage consists of synthetic image and microwave image singular values with the most significant weight. As a result, results with high predictive MSE values were obtained. The images were reconstructed using the synthetic image singular value estimates obtained from these results and the real microwave image SVD value. The similarity correlation coefficients of the images were calculated by comparing the predicted image and the microwave image with the desired synthetic image. The estimated image pixel values were magnified and brought closer to the desired dielectric value. The second stage of the thesis is aimed to design a breast phantom similar to the real breast tissue in the simulation environment and to make a dielectric estimation with the images obtained from it. Similarly, a new dataset was created from the breast phantom data. Similar to the processes performed during the previous machine learning processes, a breast phantom microwave image synthetic image and dielectric profile not included in the dataset were estimated. By replacing the real microwave image SVD values with the singular values found, the image pixel value was moved to higher values and approached the synthetic image values. The estimated image and the microwave image obtained as a result of breast phantom simulation were compared with the desired synthetic image by looking at the similarity values. As a result, it can be said that the predicted image improves the microwave image.
A compact low sar value circularly polarized wearable antenna design for 5G applications

(Graduate School, 2022-07) Gökdemir, Melih ; Yapar Akleman, Funda ; Karamzadeh, Saeid ; 504191414 ; Biomedical Engineering

Wireless Body Area Network (WBAN) is a technology that enables various devices to connect by putting them on a human body. Measurement of real-time physiological changes, medical diagnosis, and navigation are among the uses of WBAN. Monitoring the changes within the human body allows us to preserve a record of the health of patients continually, and it could be provided increased living standards for patients. In WBAN systems, wearable antennas perform the function of the transceiver structure, which allows for the transmission and reception of data and information. These antennas are designed to maintain their flexibility so that they do not restrict the mobility of the human body. Also, their structure should be strong for human body properties. A variety of flexible materials are employed as substrates in creating wearable antennas these days. This is done so that the structure of the antenna can supply flexibility. Because the human body is in a state of continual motion, it might be challenging to get the correct polarization alignment of the transceiver system, which is necessary for improved power reception. The use of circular polarization (CP) does away with the requirement that two systems be constantly aligned in order to receive maximum power. The majority of the previously represented wearable antennas are rigid, linearly polarized, massive in size, or have a very thick substrate. These characteristics make it challenging to employ these antennas in wearable applications. Specific Absorption Rate (SAR) refers to the ratio of the absorbed power to the unit mass of the tissue. Both the United States of America and the European Union have established their standards so that it is unacceptable for the power density of 1 g and 10 g of tissue to surpass specific values. The SAR value is a crucial limiting factor for wearable antennas. Because of this, a wide variety of methods are used in order to reduce the SAR value. Narrowband wearable antennas inevitably possessed a restricted bandwidth and a slow data transmission rate, particularly when coupled with communication modules. Despite this, the fifth generation (5G) of the communication spectrum helps address the shortcomings of previous generations. The 5G spectrum provides a higher and broader data transfer rate than the previous spectrum bands. The circular microstrip antenna in a coplanar waveguide structure has been chosen to operate in the 5G band. Denim and roger 5880 substrates have been used to provide flexibility and low SAR value. Denim substrate has a thickness of 0.787mm, and Roger substrate has a thickness of 0.508mm. There is a gap of 0.6 mm between the two substrates. A copper tape with a thickness of 0.035mm has been used as a conductor. A rectangular slot has been placed in the middle of the circular patch to ensure circular polarization. The performance of the coplanar waveguide antenna is simulated in free space and near the human body. The antenna showed a good fit in on-body and free space simulation. The simulation has been carried out by placing the antenna at a distance of 5 mm from the body. Although some changes have been observed in the simulation results on the body, there has been no severe change in general. There has been a slight shift in the frequency band and a light reduction in impedance matching. The use of two substrates has provided the lower SAR values. In summary, the antenna results provided circular polarization and very low SAR values in the desired band range. It has a good result in terms of impedance matching and radiation characteristics. The antenna has small dimensions and flexibility in general structure. It could be a good candidate for wearable applications.
Segmentation of breast microwave imaging using fuzzy c-mean clustering

(Graduate School, 2023) Mamizadeh, Asal ; Akduman, İbrahim ; 847155 ; Biomedical Engineering Programme

Breast cancer is the uncontrolled growth of breast cells, which grows mainly in fatty tissue and lobules. It could also spread over the blood or lymph system to other organs, causing virulent metastasis. In 2020, Female Breast cancer was the most frequently diagnosed cancer with 2.3 million cases, based on World Health Organization's (WHO) reports. According to statistics, 684 996 deaths from the disease were recorded in the same year. The most effective way to control the spread of breast cancer and reduce the number of deaths is early detection and treatment through clinical evaluation. There are several imaging techniques for screening female breast tissue abnormalities and cancer, such as Magnetic Resonance Imaging (MRI), Ultrasonography, Positron Emission Tomography (PET), and Mammography. Currently, mammography is considered to be the gold standard methodology in order to screen breast cancer. However, in accordance with the mammography restrictions, such as high radiation dose and low sensitivity in dense breasts, the mentioned method is not remarkably efficient. For this purpose, other screening methods are necessary to eliminate these restrictions. Microwave imaging (MWI) is a new modality that could potentially become a supplementary method, to already existing ones, to contribute the breast cancer diagnosis at an early stage, due to using non-ionizing radiation, safe and inexpensive technology. MWI is based on differences between the dielectric properties of cancerous and normal breast tissue. Moreover, lesion segmentation is essential for diagnosing breast cancer. To do this, specialists examine the images manually, which may lead to misdiagnosis due to human visual perception error. Also, in some cases, this process can be time-consuming. In order to solve these problems, automated segmentation methods have been utilized to reduce these errors. Clustering algorithms or unsupervised machine learning techniques could be employed to segment images to minimize human involvement in segmentation and achieve a precise detection of the cancerous lesion. In this thesis, a segmentation method is suggested to extract lesions from low-resolution MWI images with the help of the Fuzzy C-mean algorithm and statistical features. Microwave images used in the thesis were taken from a clinical study. The study's codes are all implemented in MATLAB. To evaluate the effectiveness of texture features of MWI for segmentation by FCM algorithm, three statistical features in the spatial domain i.e., intensity, entropy, and energy were used to form the feature vector. Two series of experiments were performed on the data. The first experiment included a combination of features consisting of intensity, energy, and entropy matrix of each image were form. Energy and entropy matrices were calculated for each image and combined with intensity matrices and applied to FCM. No difference was observed regarding the effect of entropy and energy matrices in segmentation with only intensity features. In the second experiment, only different combinations of intensity (i.e. color features) of the images were used. Three feature matrices were formed using red, green, and blue planes of each image, in RGB color space. A three-dimensional matrix using RGB planes, a matrix using the mean of RGB planes, and at the end, only red plane as feature matrix, were formed and applied on FCM algorithm as the first trial, separately for each image. The segmented images compare to ground truth images. The experimental results demonstrate that the segmentation method using only the red plane, to segment the dark red region of the image, which represents the highest values of dielectric activity of the tumor, outperforms the other two methods in the case of most overlapping with the ground-truth image. Also, in respect of specificity, this method has performed better compared to multi-channel methods. However, in terms of sensitivity, the red single-channel method does not provide the expected reliable results. Finally, the achieved size of the segmented tumor in each trial was compared with the size of the tumor in conventional imaging measured by a specialist. In certain sizes, there are acceptable achievements, but for satisfying results, existing algorithms need more improvements.
Denetimsiz derin öğrenme kullanılarak dijital meme tomosentezi görüntülerinde gürültünün giderilmesi

(Lisansüstü Eğitim Enstitüsü, 2023) Özcan, Osman Alpcan ; Yıldırım, İsa ; 833551 ; Biyomedikal Mühendisliği Bilim Dalı

Meme kanseri, kadınlarda en yaygın kanser türüdür ve genellikle süt bezi dokusu hücrelerinin kontrolsüz ve aşırı çoğalması sonucu ortaya çıkar. Erken aşamada teşhis edilmezse ölümcül hale gelebilir. Erken aşama teşhisinde en çok tercih edilen görüntüleme yöntemi olan mamografi, 2 boyutlu (2D) bir görüntüleme yöntemidir. 2D yöntemler doğaları gereği bir projeksiyondan görüntü oluştururlar ve bu, dokuların üst üste binmesi nedeniyle yanlış pozitif ve yanlış negatif oranlarının artmasına yol açar; bu oranın artması da ölüm oranlarını artırır. Bu durumu önlemek için 3D görüntüleme yöntemleri önerilmiş ve kullanımları artmıştır. Dijital meme tomosentezi (DBT), 3 boyutlu (3D) bir görüntüleme yöntemi olarak tercih edilir. DBT, sınırlı açılarda alınan projeksiyonlarla birlikte 3D bir görüntü oluşturur ve düşük radyasyon dozlarıyla çalışır. 2D görüntülemede oluşan dokuların üst üste binmesi sorunu aşılmış ve böylece dokular daha ayırt edilebilir hale gelmiş ve radyologların erken teşhis oranları artmıştır. Ancak, DBT görüntülerinde sınırlı açı ve düşük radyasyon dozu nedeniyle her projeksiyonun gürültü içereceği ve gürültünün rekonstrüksiyon görüntüsünde artmış şekilde görüneceği kaçınılmazdır. Gürültü giderimi için birçok matematiksel yöntem önerilmiş ve belirli bir ölçüde başarılı oldukları gözlemlenmiştir, ancak hala tatmin edici bir performans sağlama konusunda eksiklikleri vardır. Yapay zekâ ve derin öğrenme ağlarının gelişimi ve yaygınlaşmasıyla birlikte, tıbbi görüntü işleme alanında kullanımı da artmıştır. Bu bağlamda, derin öğrenme ağları, tıbbi görüntülerde bir gürültü giderici olarak mekânsal düzenleyicilere alternatif olarak önerilmiştir. Bu çalışmada, bir denetimsiz gürültü giderici sinir ağı, DBT görüntüleri üzerinden gürültüleri ortadan kaldırmak için geliştirilmiştir. Önerilen yöntemin performansını analiz etmek için iki farklı veri seti kullanılmıştır. İlk veri seti kanser görüntü arşivinden BSC-DBT (Meme Kanseri Taraması- Dijital Meme Tomosentezi) veri setidir. Bu veri seti, normal, aksiyon alınması gereken, biyopsi ile kanıtlanmış iyi huylu ve biyopsi ile kanıtlanmış kanser olarak etiketlenmiş 5060 hasta içermektedir. Görüntüler dilimler halinde 2 boyutlu olarak kullanılmıştır. Her kategoriden eşit ve toplam verilerin %70 eğitim verisi, %15 validasyon verisi ve %15 test verisi olacak şekilde belirlenmiştir. Bu veri setine ek olarak DBT-2D Phantom veri seti kullanılmıştır. Toplamda 2148 veri ve yine görüntüler iki boyutlu dilim görüntüsü olacak şekilde veri setinin %70'i eğitim verisi %15'i validasyon verisi ve %15 test verisi olarak organize edilmiştir. Düşük doz ve dar açı kaynaklı en belirgin gürültü şekilleri Gaussian ve Poisson gürültüleri olması sebebiyle gürültü giderici hedefi bu iki gürültü olarak belirlenmiş ve eğitim parametreleri bu gürültülerin giderilmesi üzerine belirlenerek eğitim tamamlanmıştır. Önerilen modelin başarısını değerlendirmek için CNR (kontrast-gürültü oranı), PSNR (zirve sinyal gürültü oranı) ve SSIM (yapısal benzerlik indeksi) metrikleri kullanılmıştır. Çalışma sırasında matematiksel yöntemler ile önerilen modelin başarısı metrikler üzerinde kıyaslanmış ve çalışmanın sonucunda önerilen denetimsiz gürültü giderici ağ ile belirlenen metriklerdeki iyileşmenin daha üstün olduğu gözlemlenmiştir. Görüntülerdeki niteliksel iyileşme, önerilen modelinin oldukça umut verici olduğunu göstermektedir.
Skin lesion classification with machine learning

(Graduate School, 2023) Sendel, Esra ; Yıldırım, İsa ; 783838 ; Biomedical Engineering Programme

Skin lesions are the part of the skin that has an abnormal structure and appearance compared to the surrounding areas. While some skin lesions cause only a physically bad appearance, some may have cancer features. Nowadays, skin cancer is one of the most extensive cancers among humans. Therefore, the correct detection of cancerous lesions is of great importance in the treatment of skin cancer. Skin cancers are basically divided into two main types, melanoma which is dangerous, and non-melanoma. The low contrast between the lesioned and non-lesion areas in the images of melanoma skin cancers requires expertise in the application of diagnostic methods and involves relativity among dermatologists. In addition, it is very difficult to automatically analyze human skin due to geographical and climatic effects, roughness, tonal diversity, hair and many other complex structures, and low contrast problem. However, a successful automatic analysis system to be created will help dermatologists to diagnose and speed up the process. Technology advancements have made it possible for doctors to diagnose skin cancer from dermatoscopic images using computer-aided diagnosis techniques, such as deep learning and machine learning models. In this study, image processing and machine learning techniques were used to classify skin lesions. In image processing, different mathematical algorithms have been applied to increase image quality. After an image preprocessing stage, which includes filtering the unwanted pixels in the images, image segmentation was performed using the watershed method and the lesioned regions were separated. Then, based on the ABCDT rule, feature extraction was performed with the lesions, asymmetry, border irregularity, color, diameter, and texture analysis. Texture analysis was performed based on Haralick texture properties. Finally, classification was performed with softmax regression, k-nearest neighbor (KNN), and support vector machines (SVM) algorithm. Standard performance measures called accuracy, precision, recall, and F1-score values are used to evaluate the results of the methods used for classification. When the results obtained from the classifiers are compared, it has been observed that the accuracy of the SVM classifier is higher than the softmax regression and k-nearest neighbor (KNN) algorithm. Keywords: Skin lesion, skin cancer, image processing, ABCDT analysis, machine learning, support vector machine algorithm, softmax regression, k-nearest neighbor algorithm.
Detection of liver fibrosis on slide-level labeled unstained biopsies by quantitative phase imaging and multiple instance learning

(Graduate School, 2023-02-14) Çelebi, Lütfi, Kadir ; Yıldırım, İsa ; 504191412 ; Biomedical Engineering

In recent years, advances in artificial intelligence (AI) have had a great impact in medicine, particularly in diagnostic fields such as radiology and pathology. In this context, the recognition and detection of important patterns in medical data can be made with AI, which can improve diagnostic processes, automate and make them more accurate and efficient, as well as increase sensitivity and consistency. One area where AI has great potential is digital pathology (DP), which is increasingly replacing traditional histopathological examination. In DP, tissue slides are turned into high-quality digital images and it is very convenient to perform detection, segmentation and classification tasks using AI on these data. On the other hand, biopsy specimens are stained for imaging with contrast in conventional pathology and current digital pathology. However, these methods have disadvantages such as staining errors, color differences due to different stain reagent manufacturers and different staining conditions. Quantitative phase imaging (QPI) is a very suitable method to overcome these disadvantages. Biological samples used in classical microscope examination are very thin and poorly absorb and scatter light. Therefore, no contrast occurs during imaging without staining, ie they are transparent. Whereas conventional bright field imaging measures only the amplitude, OPI allows measurement of both amplitude and phase information of the sample. Thus, transparent samples can be imaged with high contrast via QPI without any staining. QPI has great potential for use in cell biology, imaging of red blood cells, and diagnosis of diseases by histopathological examination, such as cancer. Fourier Ptychography (FP) microscopy is a computational imaging technique that can perform QPI. FP combines two classical optical techniques, Phase Retrieval and Aperture Synthesis, to obtain a complex image (amplitude and phase) of the sample by iteratively combining low-resolution images illuminated from different angles in Fourier space. FP provides two advantages: 1- It provides high resolution images while maintaining a wide field of view (FOV), 2- It acquires quantitative phase information without using complex optical interference techniques. In this study, the aim was to detect liver fibrosis using Attention-based Deep Multiple Instance Learning (MIL) from phase images of liver biopsies obtained by FP microscopy. In this framework, very large liver biopsy phase images are divided into patches and fed to the model, and the model infers healthy or diseased states at the biopsy image level. Also, the attention layer used in this architecture ensures that different attention weights are assigned to each patch in each biopsy image. Thus, besides classification at the biopsy image level, the most significant patches on the slide leading to the positive label can be found. Thus, it can provide support to the pathologist in the detection of disease-related tissue regions. One of the aims of this study is to try to eliminate the subjectivity and inconsistency of traditional histopathological examination due to staining using QPI. Another aim of the study is to train a deep learning model to accurately diagnose liver fibrosis through the MIL framework with only biopsy-level labeling without any local labeling by the pathologist on phase images of liver biopsies. In addition, the attention mechanism ensures that areas of high diagnostic value are found. Thus, this approach has the potential to overcome some of the limitations of AI in medical diagnosis, such as interpretability and explainability. Phase images of liver biopsies with FP were obtained with high contrast and high resolution. As a result of deep learning studies on these data, an average of 88% accuracy was achieved and the most important patches were detected by the deep learning model. It was confirmed by the pathologist that these regions were associated with fibrosis. The results of this study show that FP and Attention-based MIL are very promising for digital pathology.
Novel design of transducer for bone conduction use

(Graduate School, 2023-06-22) Ayvaz, Utku ; Çilesiz, İnci ; 504201418 ; Biomedical Engineering

Hearing losses are worldwide acknowledged health problems affecting overall life quality of suffering patients. Many different therapeutic prostheses were developed for these hearing traumas. One of the most preferred treatment process are hearing aids. Different types of hearing aids were developed to rehabilitate and treat people suffering from hearing traumas. One such type of hearing aid is the bone conduction (BC) hearing aid, which is helpful for indicated patients. BC has been used for many decades in hearing aids, and there are now several implantable versions. These hearing aids transmit the sound through the skull bones to the inner part of the ear, bypassing the outer and middle parts. BC hearing aids are essentially designed with a transducer principle. The transducers are used to convert energy from one form to another. This process is called as transduction as well. Bone conduction transducers transform incoming audio signals to the vibrations that is transmitted to the cochlea, which transmits converted signals to the brain through auditory nerves. We aim to find a magnetostrictive transducer design with optimized data. The obtained optimized values will be used the possible a transducer prototype design for bone conduction use. This prototype is properly able to perform together with an audio processor, microphones, receiver coil, supermagnet and a battery compartment. We investigated material options to replace piezoelectric ceramics that are frequently used in BC hearing implants. During investigations for material selection, the properties of performing effectiveness and biocompatibility of alloys were watched out. According to the literature review, Terfenol D, Galfenol, and Metglas 2714A® were identified as possible materials for bender in the transducer. The bender component of the simulated transducer is extremely critical for this study. It consists of both magnetostrictive and non-magnetostrictive alloys. Other parts of transducer are counterweight or core, coil, permanent magnet and tape. The material selections for components transducer apart from bender, inspired from other current transducer applications. ANSYS® Mechanical and Electronics environment was chosen for simulations and testing. Some mechanical parameters were determined before starting simulations by reviewing current transducer applications to create a transducer model in ANSYS®. Then, mechanical simulations were performed in the specific ranges of mechanical sizes by using this model. Mechanical dimensions were simulated and optimized regarding size and resonance frequency, similar to existing bone conduction transducers. We determined the reference resonance frequency value as inspired by previous bone conduction transducer studies. For resonance frequency optimization, modal and harmonic analysis were performed. Modal analysis was used to determine specific parameters to create a mathematical model that shows dynamic reaction of the vibrating structure. Meshing adjustments were arranged to have more efficient simulation results. We had specific boundary conditions and, they were applied in the mechanical simulations. The boundary condition adjustment was to show the effect of skull simulator on the connector. Point of mass and cylindirical support parameters were applied to the model as boundary conditions. On the other hand, 1N Force was utilized in the model as the load. As a result of modal and harmonic response analysis, it was found that 0.3 and 0.4 mm for Metglas 2714A®, and 0.4 and 0.5 mm for Galfenol and Terfenol D, were appropriate thicknesses. Furthermore, for all three materials, 20 mm length and 4.8 mm width were evaluated as appropriate in electromagnetic simulations. Electromagnetic simulations were performed by adding different types of super-strong neodymium permanent magnets and turns of the multilayer coil. Also, the optimized mechanical dimensions obtained from mechanical simulatios through ANSYS was utilized in electromagnetic simulations. Besides, mechanical dimesions of electromagnetic components were determined by studies carried out by previous similar studies. Furthermore, resistance, inductance, and the number of turns of the coil were calculated for each simulation. After evaluation, Metglas 2714A® Magnetic alloy and Terfenol D were deemed less suitable for this application because of their size and robustness. Optimized mechanical dimensions and electromagnetic parameters were suggested for ferromagnetic material to construct a bone conduction transducer prototype. It is suggested to use Galfenol alloy with 0.5 mm thickness, 20 mm length, and 4.8 mm for build a magnetostrictive transducer prototype for BC use by using a permanent neodymium magnet with 200-250 turns of the coil.
Denetimsiz derin öğrenme kullanılarak dijital meme tomosentezi görüntülerinde bulanıklığın giderilmesi

(Lisansüstü Eğitim Enstitüsü, 2023-09-25) Aydın, Müberra ; Yıldırım, İsa ; 504201409 ; Biyomedikal Mühendisliği

Her yıl binlerce kişinin ölümüne neden olan kanserin teşhisinde, tümörün doğru lokalizasyonu tedavi süreci için hayati önem taşımaktadır. Hastalığın erken teşhisi, kanserli dokunun büyümesini ve daha fazla dokuya yayılmasını önlemek için çok önemlidir. Tıbbi görüntüleme teknikleri, kanserin erken teşhisine önemli katkı sağlamaktadır. Kadınlarda kansere bağlı ölüm oranlarında en yüksek paya sahip olan meme kanserinin erken teşhisinde de görüntüleme teknolojilerinden yararlanılmaktadır. Özellikle 40 yaş üstü kadınların kendilerini kontrol etmeleri ve ailelerindeki kanser öyküsüne bağlı olarak düzenli taramalardan geçmeleri önerilmektedir. Bu tarama işlemlerinde yaygın olarak mamografi kullanılmakla birlikte, dijital meme tomosentezi (DBT) kullanımı da giderek yaygınlaşmaktadır. Mamografi iki boyutlu (2B) görüntüler oluştururken, DBT üç boyutlu (3B) görüntüler oluşturur. DBT, mamografide üst üste binme nedeniyle gizli kalmış lezyonların teşhisini mümkün kılmaktadır. Ayrıca taramanın farklı açılarda yapılması, oluşturulan görüntülerle yanlış pozitif oranlarını azaltılmasını sağlamaktadır. Taramaların sınırlı açılarla gerçekleştirilmesi, önlenemeyen hasta hareketleri ve dedektör odaklama aşamasındaki aksaklıklar DBT görüntülerinde bulanıklık artefaktlarına neden olabilmektedir. Bulanıklık etkisi, görüntü kalitesini gözle görülür şekilde düşürerek uzmanın görüntüdeki anormallikleri fark etme olasılığının da düşmesine sebep olmaktadır. Bahsi geçen dezavantajların azaltılması için, farklı modalitelerde çeşitli görüntü restorasyon teknikleri uygulanmaktadır. Bu çalışmada, DBT görüntülerinde tanısal doğruluğu azaltan bulanıklık etkisinin önüne geçebilmek için bir derin öğrenme modeli önerilmektedir. Temel gerçek görüntülere erişimin mevcut olmaması veya zor bulunması nedeniyle, model denetimsiz olarak tasarlanmıştır. Önerilen modelin eğitimi için kanser görüntüleme arşivinden alınan ve 5060 hastaya ait normal, işlem yapılabilir, biyopsi ile kanıtlanmış iyi huylu ve biyopsi ile kanıtlanmış kanser olarak etiketlenmiş görüntüleri içeren Meme Kanseri Taraması-Dijital Meme Tomosentezi (BSC-DBT) veri seti kullanılmaktadır. İşlemci kapasitesinin az olması ve dicom görüntülerin saklanması için çok fazla bellek gerekmesinden ötürü görüntüler png uzantılı olarak kaydedilmektedir. Biçimleri değiştirilen görüntüler ilk olarak kontrast uyarlamalı adaptif histogram eşitleme (CLAHE) ve yeniden boyutlandırma işlemlerini içeren bir ön işleme sürecine tabi tutulmaktadır. Veri setinden seçilen 2600 görüntü eğitim verisi, 350 görüntü validasyon verisi ve 260 görüntü ise test verisi olarak ayrılmıştır. Seçilen verilere farklı seviyelerde kernel boyutları ve sigma değerleri ile Gaussian bulanıklık filtreleri uygulanmıştır. AE (Autoencoder) ve GANs (Generative Adversarial Networks) modellerinin art arda kullanılması ile tasarlanan bu model 2B konvolüsyon, batch normalizasyon, LeakyReLU aktivasyonu, MaxPool ve Dropout katmanlarını içermektedir. Tasarlanan model, bulanık görüntüyü temel alarak orijinal görüntüyü yeniden oluşturmayı amaçlar. Bulanık görüntü, gerçek dünya uygulamalarında yaygın olarak bulunan düşük kaliteli veya bozulmuş görüntülerin modellenmesi amacıyla kullanılmaktadır. Önerilen modelin başarısı kontrast-gürültü oranı (CNR), ortalama mutlak hata (MAE) ve yapısal benzerlik indeksi (SSIM) ve uzman radyolog tarafından yapılan yorumlar ile değerlendirilmiştir. Nicel değerlendirmeler sonucunda bulanıklıktan arındırılan görüntülerin, bulanık görüntülere kıyasla orijinal görüntülere daha benzer olduğunu ortaya koymaktadır. Hem nicel sonuçlar hem de nitel değerlendirmeler, önerilen modelin DBT görüntülerindeki bulanıklaştırma artefaktlarını ele almada oldukça umut verici olduğunu ve bunun da teşhis doğruluğunu artırmasının beklenebileceğini göstermektedir.
Sagittal posture analysis using wearable vivaldi antenna

(Graduate School, 2024-06-11) Kaya, İlke ; Abdolsaheb Yılmaz, Tuba ; 504211405 ; Biomedical Engineering

A healthy posture is one that allows the body to spend minimum energy. Poor posture can cause back pain, balance disorders, and breathing difficulties. In addition, unhealthy posture brings mental disorders. For this reason, correct evaluation of posture becomes important. There are many methods to evaluate posture. However, traditional methods produce qualitative results because they depend on observation. Although radiography is considered one of the most efficient posture assessment methods, it has negative health effects. This study aimed to evaluate sagittal posture using two identical Vivaldi antennas. The studies were carried out in a simulation environment and an experimental environment and compared. In simulation studies, one of the antennas was kept fixed in free-space while the other was positioned at certain angles. S parameters taken from different angles were compared. Additionally, muscle phantom has been added at certain frequencies. The muscle phantom was bent at certain angles along with the antennas and the S parameters were compared. In experimental studies, two antennas were positioned opposite each other, similar to simulation studies. While one antenna was kept fixed, the other was rotated at certain angles with the help of a goniometer to obtain S parameters. The studies were repeated with a female volunteer. The volunteer's T4 and T12 vertebreas were found manually and marked. Antenna directives are attached to the marked points, facing each other. With the help of an inclinometer application on the phone, the volunteer's posture angles were adjusted and S parameters were obtained. The measurements were repeated by adding foam between the antenna and the body. All experimental studies carried out in the laboratory were repeated with the antipodal Vivaldi antenna and the results were compared. Looking at the simulation results, there are differences between the S21 parameters taken from different degrees of the antennas. However, when the antennas are directly attached to the body, it is seen that the S21 responses decrease and the noise increases. In addition, due to the rigid structure of Vivaldi antennas, roundings are seen on the body in different postures, creating unwanted gaps between the antenna and the body. These gaps affect the individual performance of the antennas. When looking at the S21 parameters of the Vivaldi antenna after the Gaussian filter applied to the S parameters, it can be seen that 4 out of 5 angles can be separated. This does not apply to the antipodal Vivaldi antenna. It was observed that when foam was added between the body and the antenna, there was still noise, but the individual antenna performances were more stable, and the gaps between the body and the antenna could be kept more stable. In the study conducted with Vivaldi antennas, it was seen that all 5 angles could be easily separated when the Gaussian filter was applied. Although it is seen that 5 angles can be separated with antipodal Vivaldi antennas, the differences in the Vivaldi antenna are more obvious.
1H-MRSI of the deep gray matter structures in patients with amyotrophic lateral sclerosis

(Graduate School, 2024-08-19) Torlak, Meryem ; Yıldırım, İsa ; Işık Öztürk, Esin ; 504211408 ; Biomedical Engineering

Amyotrophic Lateral Sclerosis (ALS) is a life-threatening disease that causes degeneration in nerve cells in the brain and spinal cord. In ALS patients, voluntary control of the arms and legs are affected. Currently, there is no cure for ALS. The primary goal of treatment is to manage symptoms to the greatest extent possible. Magnetic Resonance Spectroscopy (MRS) is employed to assess the concentration of metabolites in specific brain regions. This method has not been fully explored to understand the metabolic deficits in individuals diagnosed with ALS. It is crucial to understand the metabolic effects of ALS through various brain regions, particularly at the deep gray matter structures. 1H-MRSI data for 30 ALS patients, with a mean age of 57.8±9.55 years (17 females, 13 males) and 27 healthy controls, with a mean age of 48.44±10.5 years (16 females, 11 males) were acquired using a clinical 3T Siemens magnetic resonance imaging (MRI) scanner equipped with a multivoxel semi-LASER (sLASER) sequence (TR=1700ms, TE=40ms, VOI=10x10x15 mm3). In this study it is aimed to assess the metabolic differences between ALS patients and healthy controls (HC) at the thalamus, putamen, caudate and white matter regions. To achieve this, multivoxel magnetic resonance spectral data from ALS patients and HC were analyzed and metabolites Glx, GSH, tNAA, tCho, and Tau, as well as their ratio to tCr were quantified using LCModel. A Cramer-Rao lower bound (CRLB) threshold of less than 30 was employed to identify properly fitted metabolites. The metabolite peak ratios at the thalamus, putamen, caudate, and white matter regions were then compared between these two groups. A Wilcoxon signed-rank test was used to detect statistically significant differences in metabolite peak ratios between the left and right hemispheres at the thalamus, putamen, caudate, and white matter regions. The Mann-Whitney rank-sum test was used to evaluate metabolite peak ratio differences between ALS patients and healthy controls at some deep gray matter structures and white matter. MNI152 brain atlas was consulted to define the thalamus, putamen, and caudate regions. In conclusion, in this study, it was observed that ALS patients had a higher tCho/tCr ratio at the putamen compared to HC. Additionally, there was a trend for a lower Glx/tCr ratio at the left putamen of ALS patients compared to HC. ALS patients also showed a trend towards higher tCho/tCr and GSH/tCr ratios at the left caudate compared to HC. In ALS patients, higher Glx/tCr ratios were observed in the right thalamus and putamen compared to the left side. A trend towards a lower Glx/tCr ratio at the right caudate of ALS patients was observed compared to the left side. Additionally, ALS patients showed a lower tNAA/tCr ratio at the right caudate compared to the left side. In HC, higher Glx/tCr ratios were observed at the right thalamus compared to the left side. A trend towards higher tNAA/tCr and Glx/tCr ratios at the right putamen and right white of HC compared to the left sides were also observed. In this study, metabolic alterations were detected at the deep gray matter regions associated with executive function and behavior. Results of this study showed increased gliosis because of increased tCho/tCr ratio in ALS patients, a response to oxidative stress because of elevated GSH/tCr ratio in ALS patients, and deficiency in glutamate within these structures in individuals with ALS. The results of this study can contribute to a deeper understanding of ALS. Moreover, the findings obtained from this study suggest that MRS is a significant diagnostic and monitoring tool for neurodegenerative diseases such as ALS.

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