Ali Emre Kavur

Ali Emre Kavur

Doctor of Engineering

About

27
Publications
7,260
Reads
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309
Citations
Introduction
Ph.D in Electrical Engineering. Topics: Medical Imaging, Challenges in Biomedical Image Analysis, Classifier Ensembles.
Additional affiliations
July 2021 - present
German Cancer Research Center
Position
  • Software Engineer
March 2019 - present
Bangor University
Position
  • Academic Visitor
Description
  • Optimized Fusion of Liver Segmentation Algorithms Supervisor: Prof. Ludmila Kuncheva
January 2017 - February 2019
Dokuz Eylul University
Position
  • Scholar - (TUBITAK EEEAG 116E133)
Description
  • Development of Brushlet Transform Based Parametric Transfer Function Specification and Segmentd Data Compression Methods and Their Integration to a DICOM Compatible Presentation State Object for Effective 3D Visualization
Education
August 2015 - May 2020
Dokuz Eylul University
Field of study
  • Electrical-Electronics Engineering
January 2015 - September 2018
Izmir Katip Celebi Universitesi
Field of study
  • Biomedical Technologies
September 2011 - August 2014
Dokuz Eylul University
Field of study
  • Electrical-Electronics Engineering

Publications

Publications (27)
Preprint
Full-text available
Biomedical image analysis algorithm validation depends on high-quality annotation of reference datasets, for which labeling instructions are key. Despite their importance, their optimization remains largely unexplored. Here, we present the first systematic study of labeling instructions and their impact on annotation quality in the field. Through c...
Preprint
Full-text available
The field of automatic biomedical image analysis crucially depends on robust and meaningful performance metrics for algorithm validation. Current metric usage, however, is often ill-informed and does not reflect the underlying domain interest. Here, we present a comprehensive framework that guides researchers towards choosing performance metrics in...
Article
Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. The proposed model extends standard conditional generative adversarial networks. Additionally to the d...
Article
Full-text available
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the perf...
Article
This paper presents the design, implementation and polymer nanocomposite mixing application of a robust spatiotemporal chaotic delta robot. Blending fluids efficiently is a vital process for the preparation of graphene nanocomposite mixing. The most commonly used mixing materials are polymeric materials that need to be blended in non-Newtonian flui...
Preprint
Full-text available
Despite the widespread use of deep learning methods for semantic segmentation of images that are acquired from a single source, clinicians often use multi-domain data for a detailed analysis. For instance, CT and MRI have advantages over each other in terms of imaging quality, artifacts, and output characteristics that lead to differential diagnosi...
Article
Background: DICOM standard does not have modules that provide the possibilities of 2D Presentation States to 3D. Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation result...
Preprint
Segmentation of the liver from 3D computer tomography (CT) images is one of the most frequently performed operations in medical image analysis. In the past decade, Deep Learning Models (DMs) have offered significant improvements over previous methods for liver segmentation. The success of DMs is usually owed to the user's expertise in deep learning...
Preprint
Full-text available
Objective : Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. Methods: The proposed model extends standard conditional generative adversarial networks....
Preprint
Full-text available
[https://arxiv.org/abs/2001.06535] Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL mod...
Article
Full-text available
ABSTRACT Objectives: To obtain a liver prototype by using a 3 dimensional printer for preoperative evaluation of vascular structures (hepatic artery, portal vein, hepatic vein) based on living donor candidate’s angiographic computed tomography data in living donor liver transplantation. Materials and Methods: First, we obtained angiographic compute...
Article
Purpose: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging. Methods: A total of 12 (6 semi-, 6 full-aut...
Poster
Full-text available
The aim of this work is analyzing fusion of different automatic segmentation methods to reach the superior results by using ensemble algorithms. A total of 6 deep models were examined in terms of their complementary and diversity on liver segmentation problem. Then, two fusion methods were applied to the segmentation results.
Poster
Full-text available
The aim of this work is the comparison of the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of wellestablished semi-automatic (interactive) methods for segmenting liver in living liver transplant donors at computerized tomography (CT) imaging. These results were prepared for o...
Article
Biyomedikal sinyaller içinde Elektromiyografinin (EMG) incelenmesi popüler bir araştırma alanıdır ve rehabilitasyon tasarım sistemleri için oldukça önemidir. Bu çalışmada kullanılan yöntemler sırasıyla; i) insan üzerinden iskelet kasının sinir yolu ile uyarılarak gerçek zamanlı el parmaklarına ait EMG verilerinin alınması, ii) elde edilen EMG işare...
Conference Paper
Full-text available
Recently examination of biomedical signals is popular studies. Examination of biomedical signals is interdisciplinary field of study. In this study, DC motor is controlled using real time Electromyography (EMG) data. Real time EMG data are obtained from QNET02697-MYOELECTRIC trainer set developed by QUANSER firm for National Instrument. Arduino is...
Conference Paper
In this paper, a new blending method for Graphene nanoplatelets was developed. A chaotic system based robust delta robot was designed. Both the speed of the mixer motor and position of the propeller were chaotically changed. Performance of the systems was evaluated by a material analysis method. The results showed that the proposed method has a bet...
Poster
Full-text available
The nanocomposites is a very important family of materials to be used in various applications. However, the dispersion of nanoparticles in the polymer matrices has been the main obstacle for the wide usage of this technology. In this study, we have developed a novel nanocomposite preparation technique for water based acrylic resin enhanced with gra...
Article
Purpose: Precise extraction of aorta and the vessels departing from it (i.e. coeliac, renal, and iliac) is vital for correct positioning of a graft prior to abdominal aortic surgery. To perform this task, most of the segmentation algorithms rely on seed points, and better-located seed points provide better initial positions for cross-sectional met...
Article
Full-text available
Segmentation of abdominal organs from MRI data sets is a challenging task due to various limitations and artefacts. During the routine clinical practice, radiologists use multiple MR sequences in order to analyze different anatomical properties. These sequences have different characteristics in terms of acquisition parameters (such as contrast mech...
Article
Full-text available
Medical imaging modalities can provide very detailed and informative mappings of the anatomy of a subject. These detailed and informative mappings can be processed to extract the information of interest instead of dealing with whole data (segmentation). Since manual segmentation on each slice is time consuming, tedious and operator dependent, autom...

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Projects

Projects (2)
Project
For detailed information: https://chaos.grand-challenge.org/ CHAOS challenge has two separate but related aims: 1) Segmentation of liver from computed tomography (CT) data sets, which are acquired at portal phase after contrast agent injection for pre-evaluation of living donated liver transplantation donors. 2) Segmentation of four abdominal organs (i.e. liver, spleen, right and left kidneys) from magnetic resonance imaging (MRI) data sets acquired with two different sequences (T1-DUAL and T2-SPIR).