Yazarlar
- Adali, E.(3)
- Dundar, Tolga Turan(3)
- Duru, Nevcihan(3)
- Eker, Ayse Gul(3)
- Karaduman, Ismet(2)
- Pehlivanoglu, Meltem Kurt(2)
- Serdar Mutluer, Ahmet(2)
- Bulut, Oktay(1)
- Dogan, Ihsan(1)
- Erdem, Duygu(1)
- Ericek, Omer Burak(1)
- Kurt Pehlivanoglu, Meltem(1)
- Mutluer, Ahmet Serdar(1)
- Olcer, Ercan(1)
- Ozturk, Candide(1)
- Şevik Eliçora, Sultan(1)
Yayınlar
- MakaleYalnız künye2026Scopus SJRQ2Clinical Characteristics and Management Outcomes of Pediatric Temporal Bone Fractures: A Retrospective Cohort Study
Bulut, Oktay; Şevik Eliçora, Sultan; Erdem, Duygu
Background: – Pediatric temporal bone fractures may lead to facial paralysis, hearing loss, and cerebrospinal fluid (CSF) leakage. Understanding their clinical and radiologic characteristics is essential for effective management. Methods: – This retrospective, single-center descriptive study included 50 pediatric patients diagnosed with temporal bone fractures between 2013 and 2020. Demographic data, mechanisms of injury, fracture types, facial nerve function, CSF leakage, and audiological findings were analyzed. Fractures were classified according to the McHugh system (longitudinal, transverse, and mixed) and the Kelly-Tami classification [otic capsule sparing (OCS) versus otic capsule violating (OCV)]. Hearing was evaluated using auditory brainstem response (ABR) in children under 6 years and pure-tone audiometry (PTA) with age-appropriate behavioral techniques in older children. Results: – The cohort included 50 patients (mean age: 7.8±3.8 years; 64% boys). Falls were the leading cause of injury, while high-energy traumas such as gunshot and penetrating orbital injuries were rare but associated with complete facial paralysis and total hearing loss. Most fractures were longitudinal and OCS, showing favorable recovery, whereas OCV fractures caused irreversible deficits. CSF leakage occurred in 4 patients, all resolving spontaneously. Surgery was required only for persistent conductive hearing loss, and ossicular reconstruction restored normal hearing. Conclusion: – Pediatric temporal bone fractures are mostly OCS and longitudinal, typically caused by falls. OCV fractures are uncommon but linked to irreversible facial nerve and cochlear injury.
- BildiriYalnız künye2025Brain Tissue Classification from Hyperspectral Images Using Convolutional Neural Networks
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.
AtıfScopus1
İncele - BildiriYalnız künye2025A Review of Hyperspectral Imaging in Neurosurgery: Datasets and Computational Approaches
Ozturk, Candide; Eker, Ayse Gul; Pehlivanoglu, Meltem Kurt; Karaduman, Ismet; Olcer, Ercan; Duru, Nevcihan; Dundar, Tolga Turan; Mutluer, Ahmet Serdar; Dogan, Ihsan; Ericek, Omer Burak
Hyperspectral imaging (HSI) is an imaging method that differs from traditional methods by recording the light reflection and absorption properties of each point across a wide color range. HSI enables the non-invasive determination of chemical and structural differences in tissues with high accuracy. In the domain of medicine, hyperspectral imaging represents a novel methodology that facilitates novel approaches in brain surgery, including the distinction of tumors and blood vessels, cancer diagnosis, and the monitoring of tissue oxygen levels and blood flow during surgical procedures. Additionally, hyperspectral imaging has a wide range of applications in various fields, including agriculture, food, environment, geology, and defense.This review comprehensively examines current datasets, application areas, and computational approaches related to the use of hyperspectral imaging technology in brain surgery. HSI enables the precise differentiation of different brain tissues such as tumors, vessels, functional areas, and the spine by collecting data across a wide spectral band range for each pixel. Recent studies in the literature demonstrate that machine learning and deep learning-based methods can achieve high accuracy rates in tissue segmentation and the detection of pathological structures in brain surgery. Additionally, datasets developed at different centers and involving various patient groups provide an important foundation for enhancing the clinical efficacy of HSI.While HSI boasts significant advantages in terms of its non-invasiveness and real-time analysis potential, which benefit surgeons during intraoperative decision-making processes, challenges such as data size, computational costs, and device integration pose barriers to its routine use. In the future, it is anticipated that these limitations will be overcome through the development of efficient algorithms, AI-supported systems, and multi-center validation studies. In conclusion, HSI technology exhibits considerable potential to become a standard intraoperative instrument in neurosurgery, driven by advancing computational methods and increasing clinical validation.
AtıfScopus1İncele - 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.