Development and Implementation of a Deep Learning-Based Fully Automated Approach for Automatic Detection and Measurement of Aortic Valve Calcium Score from Echocardiographic Images (TUBITAK 1001) - Ongoing project

Researcher: Dr. Ögr. Üyesi Elif Baykal Kablan

Funded Student(s): Merve Nur Çakir, Sefa Keklik

Supervisor: Prof. Dr. Murat Ekinci

Aortic valve calcium score is widely used in the diagnosis, treatment, and follow-up of aortic stenosis and in determining the risk of coronary artery disease. Current guidelines recommend considering the aortic valve calcium score, especially in the diagnosis of low-flow and low-gradient aortic stenosis. The gold standard method for determining the aortic valve calcium score is computed tomography (CT). Agatston score is a semi-automatic method used routinely to calculate calcium score in patients undergoing cardiac CT. In Agatston scoring, the calcium area is multiplied by a coefficient determined according to the CT density. It is a method whose results vary depending on the extraction technique and is affected by minor changes in slice thickness. However, since it is the first described and the most widely used method for many years, it is still frequently preferred to benefit from the data in the existing literature. On the other hand, CT is an expensive and radiation exposing examination. Echocardiography (ECHO), which is a cheaper and radiation-free ultrasonographic method, can also be used instead of CT. There are several studies to measure the aortic calcium score with (ECHO), but all these studies are observational and semi-quantitative. In addition, the aortic valve calcium score, which is evaluated with ECHO, is a visual evaluation and does not provide an objective result, as there will be intra-observer and inter-observer diagnostic differences. Therefore, automatic measurement of the aortic valve calcium score is important.

In this project, firstly, R&D studies will be conducted to develop deep learning approaches for estimating aortic valve calcium score from ECHO images, based on calcium scores obtained from CT images by Agatston method. In this way, it is aimed to obtain the aortic valve calcium score more objectively, faster, easier, less costly, and fully automatically without exposure to radiation. The fact that the patient is currently undergoing both ECHO and CT for the diagnosis of aortic stenosis exposes the patient to extra radiation. Another study we will carry out for this purpose will be the production of artificial CT images from ECHO images. Thus, the aortic valve calcium score will be determined on the CT images to be produced without exposing the patient to extra radiation. As a result of this study, it will be investigated how close to the calcium scores obtained from real CT images can be achieved with ECHO alone or with ECHO + artificial CT.

In line with these purposes;

The originality and contributions of the project can be summarized as follows: Since the project goals and studies can be a universal study in the echocardiography literature, it will provide us with important gains in national knowledge, in the direct application of interdisciplinary academic R&D studies and in the international arena.


Automatic Light Microscopy Scanning and Analysis based on Computer Vision and Machine Learning for Cytopathologic Differential Diagnosis of Malignant Neoplasia and Reactive Mesothelial Hyperplasias (TUBITAK 1001) - Finished project

Principal Investigator: Prof. Dr. Murat Ekinci

Funded Student(s): Elif Baykal Kablan, Hülya Dogan

Cytomorphological screening, performed by experts under light microscope, reguires scanning all cells in the field, determining whether the cells are malignant, benign or atypical and detailed anaysis of these cells. Differential diagnosis of malignant neoplasia and reactive mesothelial hyperplasia may be difficult even for experienced cytopathologist. Also in undiagnosed cases, it is utilized from some helper time-consuming techniques such as histochemical, immunohistochemical and electron microscopy.

In this project, two and three dimesinonal (2D/3D) high resolution panoramic imaging will be obtained with scanning all field of specimen enabling optimal focusing for medical light microscope. Cell nuclei detection, cell (nuclei and stoplysm) segmentation and cell classification will be performed by developing novel computer vision and machine learning approaches in this work. Adding machine learning and compter vision approaches on human-computer interaction, success and reliability of the system about differential diagnosis of malignant and reactive cells will be increased.

Main goals of the project can be summarized as follows:

The novelties in the proposed project can be listed as in below. Project objectives and studies can be a universal qualitative study in medical microscopic imaging and analysis literature, which will lead to national knowledge accumulation, transfer of academic R & D work to direct implementation, and significant gains on the international arena.


Avuç Izi Biyometrik Özelligine Dayali Otomatik Kisi Tanima Sistemi (TUBITAK)