Computer Science, Artificial Intelligence, Computer Vision and Pattern Recognition, Cancer Research
29
Scopus Publications
300
Scholar Citations
9
Scholar h-index
9
Scholar i10-index
Scopus Publications
An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study Abdulkerim Capar, Dursun Ali Ekinci, Mucahit Ertano, M. Khalid Khan Niazi, Erva Bengu Balaban, Ibrahim Aloglu, Meryem Dogan, Ziyu Su, Fugen Vardar Aker, Metin Nafi Gurcan Plos One, 2024 Breast cancer, a widespread and life-threatening disease, necessitates precise diagnostic tools for improved patient outcomes. Tumor-Infiltrating Lymphocytes (TILs), reflective of the immune response against cancer cells, are pivotal in understanding breast cancer behavior. However, inter-observer variability in TILs scoring methods poses challenges to reliable assessments. This study introduces a novel and interpretable proof-of-principle framework comprising two innovative inter-observer agreement measures. The first method, Boundary-Weighted Fleiss’ Kappa (BWFK), addresses tissue segmentation predictions, focusing on mitigating disagreements along tissue boundaries. BWFK enhances the accuracy of stromal segmentation, providing a nuanced assessment of inter-observer agreement. The second proposed method, the Distance Based Cell Agreement Algorithm (DBCAA), eliminates the need for ground truth annotations in cell detection predictions. This innovative approach offers versatility across histopathological analyses, overcoming data availability challenges. Both methods were applied to assess inter-observer agreement using a clinical image dataset consisting of 25 images of invasive ductal breast carcinoma tissue, each annotated by four pathologists, serving as a proof-of-principle. Experimental investigations demonstrated that the BWFK method yielded gains of up to 32% compared to the standard Fleiss’ Kappa model. Furthermore, a procedure for conducting clinical validations of artificial intelligence (AI) based cell detection methods was elucidated. Thoroughly validated on a clinical dataset, the framework contributes to standardized, reliable, and interpretable inter-observer agreement assessments. This study is the first examination of inter-observer agreements in stromal segmentation and lymphocyte detection for the TILs scoring problem. The study emphasizes the potential impact of these measures in advancing histopathological image analysis, fostering consensus in TILs scoring, and ultimately improving breast cancer diagnostics and treatment planning. The source code and implementation guide for this study are accessible on our GitHub page, and the full clinical dataset is available for academic and research purposes on Kaggle.
Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features Nurullah Calik, Abdulkadir Albayrak, Asli Akhan, Ilknur Turkmen, Abdulkerim Capar, Behcet Ugur Toreyin, Gokhan Bilgin, Bahar Muezzinoglu, Lutfiye Durak-Ata IEEE Journal of Biomedical and Health Informatics, 2023 Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.
Combined segmentation and classificationbased approach to automated analysis of biomedical signals obtained from calcium imaging Gizem Dursun, Dunja Bijelić, Neşe Ayşit, Burcu Kurt Vatandaşlar, Lidija Radenović, Abdulkerim Çapar, Bilal Ersen Kerman, Pavle R. Andjus, Andrej Korenić, Ufuk Özkaya Plos One, 2023 Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy.
A Statistical Focusing Metric for Fluorescent Microscopy Abdulkerim Capar, Ramazan Cagac, Nurcan Komutan 2022 30th Signal Processing and Communications Applications Conference Siu 2022, 2022 Autofocusing has critical importance for imaging systems with motorized microscopes in healthcare. It is not possible to diagnose on a picture that captured out of focus. In the literature, it has been observed that there are limited studies on focusing methods specific to fluorescent microscopes. In this study, a focus metric is proposed special to fluorescent microscope images. The proposed metric evaluates the responses generated by gradient filters of varying kernel size at a pixel point, and takes into account their standard deviations. The proposed method was tested on lung and breast tissue samples obtained with fluorescent microscope, and experimental results were reported. It is shown that the developed method overperforms the local gradient filters and produces an average error of 0.16 levels.
A multi-spectral myelin annotation tool for machine learning based myelin quantification Abdulkerim Çapar, Sibel Çimen, Zeynep Aladağ, Dursun Ali Ekinci, Umut Engin Ayten, Bilal Ersen Kerman, Behçet Uğur Töreyin F1000research, 2020 Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.
Detection methods of salient regions in super-resolution based on sparse representation Ömer Berk, Gülcihan Özdemir, Behçet Uğur Töreyin, Abdulkerim Çapar Tiptekno 2019 Tip Teknolojileri Kongresi, 2019 Achieving successful results with the sparse representation in super-resolution increases the interest in the field. The sparse representation model, which is an important method in super-resolution, consists of image patches, a correct dictionary and a sparse linear combination of the elements of this dictionary. At this point, the super-resolution successfully reflects the sparse pattern by obtaining high-resolution images with the sparse pattern from low-resolution image patches. The detection of image regions is critical here. In the proposed method, the successes of the results are compared by using Fuzzy C-Means Clustering and Hue-Saturation-Value (HSV) Based Segmentation methods for determination of these regions.
Myelin segmentation in fluorescence microscopy images Sibel Çimen Yetiş, Dursun A. Ekinci, Ertan Çakir, Ender M. Ekşioğlu, Umut E. Ayten, Abdulkerim Çapar, B. Uğur Töreyin, Bilal E. Kerman Tiptekno 2019 Tip Teknolojileri Kongresi, 2019 Myelin sheath, wrapped around axons, allows rapid neural signal transmission, and degeneration of myelin causes various neurodegenerative diseases, such as, Multiple Sclerosis (MS). For candidate drug discovery, it is essential to quantify myelin. This requires tedious expert labor comprising myelin labelling on microscopic fluorescence images, usually acquired by confocal microscopes. In this study, semantic segmentation based automatic myelin segmentation on fluorescence microscopy images was introduced. Three-channel and three-dimensional fluorescence images of mouse stem cell derived neuron and oligodendrocyte co-cultures were labeled by an expert. The images were divided into patches for training and the labels corresponded to each patch were acquired. A data set of 11552 patches was used for training to identify myelin and non-myelin regions. In the data set, myelin detection performances of semantic segmentation technique were evaluated using 3 different learning algorithms. The highest accuracy value of 97.32 percent was achieved by using “RMSprop” learning algorithm with a group size of 8 and after 250 epochs. Results suggested that the proposed myelin segmentation method was suitable for detecting myelin. Thus, the outlined myelin segmentation method has the potential to be incorporated into remyelination drug screens.
Shape recognition by voting on fast marching iterations Abdulkerim Capar, Muhittin Gokmen Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Affine invariant gradient based shape descriptor Abdulkerim Çapar, Binnur Kurt, Muhittin Gökmen Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
Comparison of SVM and ANN performance for handwritten character classification Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference Siu 2004, 2004
Linear dimension reduction methods in character recognition systems Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference Siu 2004, 2004
A Turkish handprint character recognition system Abdulkerim Çapar, Kadim Taşdemir, Özlem Kıłıc, Muhittin Gökmen Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2003
RECENT SCHOLAR PUBLICATIONS
Revisiting Reconstruction Likelihood: Variational Autoencoders for Biological and Biomedical Data Clustering A Korenić, U Özkaya, A Çapar bioRxiv, 2026.04. 09.717460 , 2026 2026
An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study A Capar, DA Ekinci, M Ertano, MKK Niazi, EB Balaban, I Aloglu, M Dogan, ... Plos one 19 (12), e0314450 , 2024 2024 Citations: 3
Teaching old collections new tricks: initial findings of the ISTF herbarium digitization project A Çiftçi, R Mollman, BS Kaleli, A Çapar, O Erol Herbarium Turcicum, 26-33 , 2023 2023 Citations: 5
A multi-spectral myelin annotation tool for machine learning based myelin quantification A Çapar, S Çimen, Z Aladağ, DA Ekinci, UE Ayten, BE Kerman, ... F1000Research 9, 1492 , 2023 2023 Citations: 4
Who is More Successful in Detecting Cervical High-grade Squamous Intraepithelial Lesions in Atrophic Background? Pathologists or Artificial Intelligence? Z BAYRAMOĞLU, G DURSUN DEMİR, U Ozkaya, A Capar, T Abacioglu, ... 2023
Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging G Dursun, D Bijelić, N Ayşit, B Kurt Vatandaşlar, L Radenović, A Çapar, ... Plos one 18 (2), e0281236 , 2023 2023 Citations: 5
Aydınlık Alan Optik Mikroskop Görüntülerin Analizi A ÇAPAR Turkiye Klinikleri Biomedical-Special Topics 4 (1), 21-28 , 2023 2023
Classification of cervical precursor lesions via local histogram and cell morphometric features N Calik, A Albayrak, A Akhan, I Turkmen, A Capar, BU Toreyin, G Bilgin, ... IEEE Journal of Biomedical and Health Informatics 27 (4), 1747-1757 , 2022 2022 Citations: 5
Makine öğrenimi kullanılarak flüorışıma mikroskopi resimlerinde otomatikleştirilmiş miyelin algılamasına yönelik yöntem ve sistem DALİE BİLAL ERSEN KERMAN,ABDULKERİM ÇAPAR,BEHÇET UĞUR TÖREYİN,SİBEL ÇİMEN ... TR Patent 2019-GE-550,328 , 2022 2022
A Statistical Focusing Metric for Fluorescent Microscopy A Çapar, R Çağaç, N Komutan 2022 30th Signal Processing and Communications Applications Conference (SIU … , 2022 2022
not approved A Çapar, S Çimen, Z Aladağ, DA Ekinci, UE Ayten, BE Kerman, ... 2022
A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 3; peer review A Çapar, S Çimen, Z Aladağ, DA Ekinci, UE Ayten, BE Kerman, ... 2022
Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric N Calik, A Albayrak, A Akhan, I Turkmen, A Capar, BU Toreyin, G Bilgin, ... 2022
Automated scoring of CerbB2/HER2 receptors using histogram based analysis of immunohistochemistry breast cancer tissue images KA Kabakçı, A Çakır, İ Türkmen, BU Töreyin, A Çapar Biomedical Signal Processing and Control 69, 102924 , 2021 2021 Citations: 30
A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability A Albayrak, AU Akhan, N Calik, A Capar, G Bilgin, BU Toreyin, ... Medical & Biological Engineering & Computing 59 (7), 1545-1561 , 2021 2021 Citations: 30
A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved] A Çapar, S Çimen, Z Aladağ, D Ekinci, U AYTEN, B Kerman, B Töreyin F1000Research 9 , 2021 2021
Myelin detection in fluorescence microscopy images using machine learning SÇ Yetiş, A Çapar, DA Ekinci, UE Ayten, BE Kerman, BU Töreyin Journal of Neuroscience Methods 346, 108946 , 2020 2020 Citations: 11
Oligodendrocyte interactome in healthy and diseased nervous system. BE Kerman, Fİ Aydınlı, BK Vatandaşlar, K Yurduseven, E Vatandaşlar, ... Anatomy: International Journal of Experimental & Clinical Anatomy 14 , 2020 2020
Learning machine learning in Ca2+ fluorescence imaging P Andjus, A Korenić, M Milošević, D Bijelić, M Radojičić, K Živančević, ... 2020
Machine learning protocols and network analysis of Ca2+ fluorescence imaging after ALS IgG action on cultured astrocytes and neurons P Andjus, A Korenić, M Milošević, D Bijelić, BE Kerman, A Çapar, S Antic, ... 2020
MOST CITED SCHOLAR PUBLICATIONS
Concurrent segmentation and recognition with shape-driven fast marching methods A Capar, M Gokmen 18th International Conference on Pattern Recognition (ICPR'06) 1, 155-158 , 2006 2006 Citations: 62
Automated scoring of CerbB2/HER2 receptors using histogram based analysis of immunohistochemistry breast cancer tissue images KA Kabakçı, A Çakır, İ Türkmen, BU Töreyin, A Çapar Biomedical Signal Processing and Control 69, 102924 , 2021 2021 Citations: 30
A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability A Albayrak, AU Akhan, N Calik, A Capar, G Bilgin, BU Toreyin, ... Medical & Biological Engineering & Computing 59 (7), 1545-1561 , 2021 2021 Citations: 30
Metaphase finding with deep convolutional neural networks TBU Moazzen Y., Capar A., Albayrak A., Calik N Biomedical Signal Processingand Control 52, 353-361 , 2019 2019 Citations: 21
Comparison of SVM and ANN performance for handwritten character classification F Kahraman, A Capar, A Ayvaci, H Demirel, M Gokmen Proceedings of the IEEE 12th Signal Processing and Communications … , 2004 2004 Citations: 20
Segmentation of precursor lesions in cervical cancer using convolutional neural networks A Albayrak, A Ünlü, N Çalık, G Bilgin, I Türkmen, A Çakır, A Çapar, ... 2017 25th Signal Processing and Communications Applications Conference (SIU … , 2017 2017 Citations: 18
Gradient-based shape descriptors A Çapar, B Kurt, M Gökmen Machine Vision and Applications 20 (6), 365-378 , 2009 2009 Citations: 14
A Turkish handprint character recognition system A Çapar, K Taşdemir, Ö Kıłıc, M Gökmen International Symposium on Computer and Information Sciences, 447-456 , 2003 2003 Citations: 13
Myelin detection in fluorescence microscopy images using machine learning SÇ Yetiş, A Çapar, DA Ekinci, UE Ayten, BE Kerman, BU Töreyin Journal of Neuroscience Methods 346, 108946 , 2020 2020 Citations: 11
A deep learning based approach for classification of CerbB2 tumor cells in breast cancer GA Tataroğlu, A Genç, KA Kabakçı, A Çapar, BU Töreyin, HK Ekenel, ... 2017 25th Signal Processing and Communications Applications Conference (SIU … , 2017 2017 Citations: 8
Affine invariant gradient based shape descriptor A Çapar, B Kurt, M Gökmen International Workshop on Multimedia Content Representation, Classification … , 2006 2006 Citations: 7
Myelin segmentation in fluorescence microscopy images SÇ Yetiş, DA Ekinci, E Çakir, EM Ekşioğlu, UE Ayten, A Çapar, ... 2019 Medical Technologies Congress (TIPTEKNO), 1-4 , 2019 2019 Citations: 6
Çok Amaçlı Gürbüz Yüz Tanıma M Gökmen, B Kurt, F Kahraman, A Çapar İstanbul Teknik Üniversitesi Bilgisayar Mühendisliği Bölümü, Tübitak Projesi … , 2007 2007 Citations: 6
Teaching old collections new tricks: initial findings of the ISTF herbarium digitization project A Çiftçi, R Mollman, BS Kaleli, A Çapar, O Erol Herbarium Turcicum, 26-33 , 2023 2023 Citations: 5
Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging G Dursun, D Bijelić, N Ayşit, B Kurt Vatandaşlar, L Radenović, A Çapar, ... Plos one 18 (2), e0281236 , 2023 2023 Citations: 5
Classification of cervical precursor lesions via local histogram and cell morphometric features N Calik, A Albayrak, A Akhan, I Turkmen, A Capar, BU Toreyin, G Bilgin, ... IEEE Journal of Biomedical and Health Informatics 27 (4), 1747-1757 , 2022 2022 Citations: 5
DeepMQ: A deep learning approach based myelin quantification in microscopic fluorescence images S Çimen, A Çapar, DA Ekinci, UE Ayten, BE Kerman, BU Töreyin 2018 26th European Signal Processing Conference (EUSIPCO), 61-65 , 2018 2018 Citations: 5
A multi-level thresholding based segmentation method for microscopic fluorescence in situ hybridization (FISH) images KA Kabakçı, A Çapar, BU Töreyin, M Akkoç, O Borazan, İ Türkmen, LD Ata 2016 24th Signal Processing and Communication Application Conference (SIU … , 2016 2016 Citations: 5
Gradyan temelli şekil bölütleme ve tanıma A Çapar, M Gökmen İTÜDERGİSİ/d 10 (3) , 2011 2011 Citations: 5
A multi-spectral myelin annotation tool for machine learning based myelin quantification A Çapar, S Çimen, Z Aladağ, DA Ekinci, UE Ayten, BE Kerman, ... F1000Research 9, 1492 , 2023 2023 Citations: 4
