Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025Scopus SJRQ1Comparison of surgical approaches to the hippocampal formation with artificial intelligence
Dundar, Tolga Turan; Pehlivanoğlu, Meltem Kurt; Eker, Ayşe Gül; Albayrak, Nur Banu; Mutluer, Ahmet Serdar; Yurtsever, İsmail; Doğan, İhsan; Duru, Nevcihan; Türe, Uğur
The relatively complex functional anatomy of the mediobasal temporal region makes surgical approaches to this area challenging. Several studies describe various surgical approaches, along with their combinations and modifications, to reach lesions of this region. Some of these surgical approaches have been compared using artificial intelligence-based approaches that can be predicted, classified, and analyzed for complex data. Several surgical approaches, such as anterior transsylvian, trans-superior temporal sulcus, trans-middle temporal gyrus, subtemporal–transparahippocampal, presigmoid-retrolabyrinthine, supratentorial-infraoccipital, and paramedian supracerebellar-transtentorial, were selected for comparison. Magnetic resonance images (MRIs) were taken according to the criteria specified by the Radiology Department. With an open-source software tool, volumetric data from cranial MRIs were segmented and anatomical structures in the main regions were reconstructed. The Q-learning algorithm was used to find pathways similar to these standard surgical pathways. The Q-learning scores among the selected pathways are as follows: anterior transsylvian (Q_A) = 31.01, trans-superior temporal sulcus (Q_B) = 25.00, trans-middle temporal gyrus (Q_C) = 28.92, subtemporal-transparahippocampal (Q_D) = 23.51, presigmoid- retrolabyrinthine (Q_E) = 27.54, supratentorial-infraoccipital (Q_F) = 27.2, and paramedian supracerebellar-transtentorial (Q _G) = 21.04. The Q-value score for the supracerebellar transtentorial approach was the highest among the examined approaches and therefore optimal. A difference was also found between the total risk score of all points with pathways drawn by clinicians and the total risk scores of the pathways formed and followed by Q-learning. Artificial intelligence-based approaches may significantly contribute to the success of the surgical approaches examined. Furthermore, artificial intelligence can contribute to clinical outcomes in both preoperative surgical planning and intraoperative technical equipment-assisted neurosurgery. However, further studies with more detailed data are needed for more sensitive results.
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İncele - MakaleAçık Erişim2024Scopus SJRQ1BrainPixGAN: Generating intraoperative MRI images with mask-based generative networks
Eker, Ayşe Gül; Pehlivanoğlu, Meltem Kurt; Duru, Nevcihan; Dündar, Tolga Turan
In recent years, efforts to enhance precision in brain tumor surgeries have focused on using artificial intelligence (AI) with medical imaging. This involves integrating AI with medical imaging. This study aimed to generate a tumor-free MRI by using Generative Adversarial Networks (GANs) to establish a relationship between preoperative magnetic resonance imaging (MRI) and resection cavity segmentation masks obtained from intraoperative ultrasound (IOUS) data. For cavity segmentation, U-Net and U-Net with transfer learning were used, with the U-Net + EfficientNetB7 model achieving a high dice score of 97.82. The resection cavity mask was applied to preoperative MRI images using Pix2Pix, SPADE GAN, and BrainPixGAN. BrainPixGAN, incorporating transfer learning, outperformed the others, achieving SSIM 0.87, PSNR 35.89, and LPIPS 0.0037. This innovative approach represents a pioneering effort in generating GAN models for intraoperative MRI (iMRI) images using IOUS data, despite the challenges in setup and cost associated with iMRI imaging.
AtıfScopus13WoS10OpenAlex8İncele - MakaleAçık Erişim2024Scopus SJRQ2A new surgical path planning framework for neurosurgery
Kurt Pehlivanoğlu, Meltem; Ay, Eren Cem; Eker, Ayşe Gül; Albayrak, Nur Banu; Duru, Nevcihan; Mutluer, Ahmet Serdar; Dündar, Tolga Turan; Doğan, İhsan
Background: Despite using a variety of path-finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas-based segmentation for a more precise search. Our motivation comes from the literature’s shortcomings in designing and implementing path-planning methods. Since planning paths with curvatures is a complex problem that requires considering many surgical and physiological constraints, most path-planning strategies focus on straight paths. There is also a lack of studies that focus on the complete structure of the brain with the tracks, veins, and segmented areas. Instrument dependence is another inadequacy of the methods proposed in the literature. Aims: The aim of this study is to design a new surgical path planning framework that helps to plan the surgical path independently of the instrument, considers the entire structure of the brain, and allows curvilinear surgical paths. Thus, neurosurgeons can generate patient-specific possible optimal surgical pathways before the neurosurgical procedure. Materials & Methods: The proposed framework includes different path-finding algorithms (Dijkstra, A*, and their aggressive variants) that find optimal paths by taking the risk scores (surgeons assessed all the segmented regions, considering the extent of damage. In this evaluation, scores ranged from “0 to 10,” with the most critical areas receiving a score of “10,” while the least possible affected areas were assigned a score of "0") for sensitive brain areas into consideration. For the tract image processing the framework includes fractional anisotropy (FA), relative anisotropy (RA), spherical measure (SM), and linear measure (LM) methods. Results: This is the first paper to handle tracts and atlas-based segmentation of the human brain altogether under a framework for surgical path planning. The framework has a dynamic structure that gives the flexibility to add different path-finding algorithms and generate different widths of surgical pathways. Moreover, surgeons can update the score table to guarantee minimally invasive surgery. The output file format of all the extracted surgical paths is NRRD, so it can be easily visualised, analysed, or processed over the third part software tools. Discussion: In this study, we generated many possible surgical pathways then these pathways were evaluated by the surgeons the results were impressive because the framework could identify surgical pathways used in real-world surgery that correspond to the standard pathways such as anterior transsylvian, trans sulcal, transgyral, and sub-temporal. Conclusion: This study proposes a new surgical path planning framework for neurosurgery. Moreover, in the future by adding/adopting different parameters (such as operation time, and short and long-term complications after surgery) to the proposed framework, it would be possible to find new surgical pathways for difficult surgical conditions.
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