Karaduman, Ismet; Pehlivanoglu, Meltem Kurt; Eker, Ayse Gul; Duru, Nevcihan; Dundar, Tolga Turan; Serdar Mutluer, Ahmet; Adali, E.

Many of the decisions made and taken in the field of health are important for human life to be sufficient. In such an important area where human transactions are concerned, artificial intelligence-supported studies have a very important place, especially in recent years. This is the correct classification of four classes (Normal Tissue, Tumor Tissue, Hypervascularized Tissue and Background) separately with the 3D-CNN based deep learning model on the In-vivo Hyperspectral Human Brain Image dataset. For this purpose, firstly, three different data settings with 128 spectral bands were obtained from raw images, each containing 826 spectral bands, with dimension reduction methods. In this way, each data setting was made with the designs used in the 3D-CNN based deep learning model. As a result of the experimental studies, the highest F1 score of 99.69% was achieved with the 3D-CNN-based deep learning model.

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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.

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