Yazarlar
Yayınlar
- BildiriYalnız künye2025Calculating Brain Shift in Brain Tumor Surgeries: Integration of MR and Ultrasound Imaging Methods and Comparison of Feature Extraction Algorithms
Eker, Ayse Gul; Kurt Pehlivanoglu, Meltem; Duru, Nevcihan; Dundar, Tolga Turan; Serdar Mutluer, Ahmet; Adali, E.
Brain tumors are abnormal tissue masses formed as a result of uncontrolled cell proliferation in the brain, and surgery is the most common treatment method. During surgery, changes in the position of brain tissue are referred to as 'brain shift,' which prevents preoperative MR images from accurately reflecting the intraoperative situation and reduces surgical success. This study addresses the integration of MR and ultrasound (US) imaging methods to calculate brain shift and improve surgical accuracy in brain tumor surgeries. Different feature extraction algorithms such as SIFT, ORB, AKAZE, and BRISK were used to solve the alignment problem between MR and US images. Using an adaptive search strategy and 3D coordinate transformation, the amount of brain shift was calculated and real-time information was provided to surgeons. The use of the adaptive search strategy dynamically adjusted the search area according to the degree of brain shift, eliminating unnecessary regions and significantly increasing processing speed. Experimental results showed that the ORB algorithm performed best for small and moderate shifts (e.g., 3.78 ± 1.65 mm error for small shifts), while the SIFT algorithm was more successful for large shifts (9.91 ± 2.30 mm error). This method aims to enable surgeons to make more accurate decisions during surgery and to increase patient safety. In the future, it is planned to improve the accuracy of the method by using deep learning-based approaches and larger datasets.
- BildiriYalnız künye2023Deep Learning and Transfer Learning Based Brain Tumor Segmentation; [Derin Ogrenme ve Transfer Ogrenme Tabanli Beyin Tumoru Segmentasyonu]
Eker, Ayse Gul; Pehlivanoglu, Meltem Kurt; Ince, Ibrahim; Duru, Nevcihan
In brain tumors, which are formed by the abnormal growth of cells in brain tissue, early diagnosis, and accurate treatment are crucial. Brain tumor segmentation provides experts with the ability to accurately identify brain tumors and determine their location. Through segmentation, the type, size, and shape of tumors can be determined, enabling the application of appropriate treatment. In this study, the aim was to perform brain tumor segmentation on the Brain Tumor Figshare (BTF) dataset, which includes diverse images in terms of structural complexity, viewing angles, different device usage, noise, and bias field effects, without applying any preprocessing. Basic models such as U-Net and FCN were utilized, along with transfer learning-based approaches using VGG, XceptionNet, InceptionNet, and ResNet architectures as the underlying structures in these models. According to the experimental results, the 16-layer FCN architecture with VGG- 19 as the underlying structure achieved the highest score with a dice score of 0.9169.
AtıfScopus12
İncele - BildiriYalnız künye2023Brain Tumor Classification Using Deep Learning Algorithms; [Derin Ogrenme Algoritmalan ile Beyin Tiimoru Siniflandirma]
Pehlivanoglu, Meltem Kurt; Eker, Ayse Gul; Ince, Ibrahim; Karhan, Deniz; Duru, Nevcihan
Brain tumor classification categorizes brain tumors based on their various characteristics. It aids in determining suitable treatment strategies and predicting patient outcomes. Therefore, the objective of this study is to identify the optimal tumor classification model by utilizing various Convolutional Neural Network (CNN)-based deep learning models (including combined models) on a recently published tumor classification dataset. Furthermore, diverse image manipulation and augmentation techniques were applied and their impact on model performance was evaluated. The experimental results prove that the combined deep learning models outperform other approaches in terms of performance in multi-class brain tumor classification.
AtıfScopus3İncele