Researcher:
Bektaş, Şevval Nur

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Şevval Nur

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Bektaş

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Bektaş, Şevval Nur

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2022 - 20233

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    Henle fiber layer mapping with directional optical coherence tomography
    (Lippincott Williams & Wilkins, 2022) N/A; N/A; N/A; N/A; N/A; N/A; N/A; Department of Computer Engineering; N/A; Kesim, Cem; Bektaş, Şevval Nur; Kulalı, Zeynep Umut; Yıldız, Erdost; Ersöz, Mehmet Giray; Şahin, Afsun; Demir, Çiğdem Gündüz; Hasanreisoğlu, Murat; Doctor; Undergraduate Student; Undergraduate Student; PhD Student; Doctor; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); N/A; School of Medicine; School of Medicine; N/A; School of Medicine; School of Medicine; College of Engineering; School of Medicine; Koç University Hospital; N/A; N/A; N/A; Koç University Hospital; N/A; N/A; N/A; 387367; N/A; N/A; N/A; 324533; 171267; 43402; 182001
    Purpose: To perform a macular volumetric and topographic analysis of Henle fiber layer (HFL) from retinal scans acquired by directional optical coherence tomography. Methods: Thirty healthy eyes of 17 subjects were imaged using the Heidelberg spectral-domain optical coherence tomography (Spectralis, Heidelberg Engineering, Heidelberg, Germany) with varied horizontal and vertical pupil entry. Manual segmentation of HFL was performed from retinal sections of horizontally and vertically tilted optical coherence tomography images acquired within macular 20 x 20 degrees area. Total HFL volume, mean HFL thickness, and HFL coverage area within Early Treatment for Diabetic Retinopathy Study grid were calculated from mapped images. Results: Henle fiber layer of 30 eyes were imaged, segmented and mapped. The mean total HFL volume was 0.74 +/- 0.08 mm(3) with 0.16 +/- 0.02 mm(3), 0.18 +/- 0.03 mm(3), 0.17 +/- 0.02 mm(3), and 0.19 +/- 0.03 mm(3) for superior, temporal, inferior, and nasal quadrants, respectively. The mean HFL thickness was 26.5 +/- 2.9 mu m. Central 1-mm macular zone had the highest mean HFL thickness with 51.0 +/- 7.6 mu m. The HFL coverage that have thickness equal or above to the mean value had a mean 10.771 +/- 0.574 mm(2) of surface area. Conclusion: Henle fiber layer mapping is a promising tool for structural analysis of HFL. Identifying a normative data of HFL morphology will allow further studies to investigate HFL involvement in various ocular and systemic disorders.
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    FourierNet: shape-preserving network for Henle's fiber layer segmentation in optical coherence tomography images
    (Institute of Electrical and Electronics Engineers (IEEE), 2023) N/A; N/A; N/A; N/A; N/A; Department of Computer Engineering; Cansız, Selahattin; Kesim, Cem; Bektaş, Şevval Nur; Kulalı, Zeynep Umut; Hasanreisoğlu, Murat; Demir, Çiğdem Gündüz; PhD Student; Teaching Faculty; Undergraduate Student; Undergraduate Student; Faculty Member; Faculty Member; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; N/A; 387367; N/A; N/A; 182001; 43402
    Henle's fiber layer (HFL), a retinal layer located in the outer retina between the outer nuclear and outer plexiform layers (ONL and OPL, respectively), is composed of uniformly linear photoreceptor axons and Muller cell processes. However, in the standard optical coherence tomography (OCT) imaging, this layer is usually included in the ONL since it is difficult to perceive HFL contours on OCT images. Due to its variable reflectivity under an imaging beam, delineating the HFL contours necessitates directional OCT, which requires additional imaging. This paper addresses this issue by introducing a shape-preserving network, FourierNet, which achieves HFL segmentation in standard OCT scans with the target performance obtained when directional OCT is available. FourierNet is a new cascaded network design that puts forward the idea of benefiting the shape prior of the HFL in the network training. This design proposes to represent the shape prior by extracting Fourier descriptors on the HFL contours and defining an additional regression task of learning these descriptors. FourierNet then formulates HFL segmentation as concurrent learning of regression and classification tasks, in which estimated Fourier descriptors are used together with the input image to construct the HFL segmentation map. Our experiments on 1470 images of 30 OCT scans of healthy-looking macula reveal that quantifying the HFL shape with Fourier descriptors and concurrently learning them with the main segmentation task leads to significantly better results. These findings indicate the effectiveness of designing a shape-preserving network to facilitate HFL segmentation without performing directional OCT imaging.
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    COVID-19–associated mucormycosis: a systematic review and meta-analysis of 958 cases
    (Elsevier, 2023) Khostelidi, Sofya N.; Klimko, Nikolai; Cornely, Oliver; Zakhour, Johnny; Kanj, Souha S.; Seidel, Danila; Hoenigl, Martin; N/A; Özbek, Laşin; Topçu, Ahmet Umur; Manay, Mehtap; Esen, Buğra Han; Bektaş, Şevval Nur; Aydın, Serhat; Özdemir, Barış; Ergönül, Önder; Undergraduate Student; Undergraduate Student; Undergraduate Student; Undergraduate Student; Undergraduate Student; Undergraduate Student; Undergraduate Student; Faculty Member; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; School of Medicine; N/A; N/A; N/A; N/A; N/A; N/A; N/A; 110398
    Background: mucormycosis, a rare fungal infection, has shown an increase in the number of reported cases during the COVID-19 pandemic. Objectives: to provide a comprehensive insight into the characteristics of COVID-19–associated mucormycosis, through a systematic review and meta-analysis. Methods of data synthesis: demographic information and clinical features were documented for each patient. Logistic regression analysis was used to predict the risk of mortality. Data sources: PubMed, Scopus, Web of Science, Cochrane, CINAHL, Ovid MEDLINE, and FungiSCOPE. Study eligibility criteria: studies reporting individual-level information in patients with adult COVID-19–associated mucormycosis (CAM) between 1 January 2020 and 28 December 2022. Participants: adults who developed mucormycosis during or after COVID-19. Interventions: patients with and without individual clinical variables were compared. Assessment of risk of bias: quality assessment was performed based on the National Institutes of Health quality assessment tool for case series studies. Results: nine hundred fifty-eight individual cases reported from 45 countries were eligible. 88.1% (844/958) were reported from low- or middle-income countries. Corticosteroid use for COVID-19 (78.5%, 619/789) and diabetes (77.9%, 738/948) were common. Diabetic ketoacidosis (p < 0.001), history of malignancy (p < 0.001), underlying pulmonary (p 0.017), or renal disease (p < 0.001), obesity (p < 0.001), hypertension (p 0.040), age (>65 years) (p 0.001), Aspergillus coinfection (p 0.037), and tocilizumab use during COVID-19 (p 0.018) increased the mortality. CAM occurred on an average of 22 days after COVID-19 and 8 days after hospitalization. Diagnosis of mucormycosis in patients with Aspergillus coinfection and pulmonary mucormycosis was made on average 15.4 days (range, 0–35 days) and 14.0 days (range, 0–53 days) after hospitalization, respectively. Cutaneous mucormycosis accounted for <1% of the cases. The overall mortality rate was 38.9% (303/780). Conclusion: mortality of CAM was high, and most reports were from low- or middle-income countries. We detected novel risk factors for CAM, such as older age, specific comorbidities, Aspergillus coinfection, and tocilizumab use, in addition to the previously identified factors.