Semiautomatic Detection of Stenosis and Occlusion of Pulmonary Arteries for Patients with Chronic Thromboembolic Pulmonary Hypertension

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F22%3A00369970" target="_blank" >RIV/68407700:21460/22:00369970 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.14311/CTJ.2022.2.04" target="_blank" >https://doi.org/10.14311/CTJ.2022.2.04</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/CTJ.2022.2.04" target="_blank" >10.14311/CTJ.2022.2.04</a>

Result language

angličtina

Original language name

Semiautomatic Detection of Stenosis and Occlusion of Pulmonary Arteries for Patients with Chronic Thromboembolic Pulmonary Hypertension

Original language description

Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe lung disease defined by the presence of chronic blood clots in the pulmonary arteries accompanied by severe health complications. It is necessary to go through a large set of axial sections from Computed tomography pulmonary angiogram (CTPA) for diagnosing the disease, which is difficult and time consuming for the radiologist. The radiologist's experience plays a significant role, same as subjective factors such as attention and fatigue. In this work we pursued the design and development of the algorithm for semiautomatic detection of pulmonary artery stenoses and clots for diagnosing CTEPH, which is based on the implementation of semantic segmentation using deep convolutional neural networks. Specifically, it is about the use of the DeepLab V3 + model embedded in the Xception architecture. Within this work we focused on stenoses and clots located in larger pulmonary arteries. Anonymized data of patients diagnosed with CTEPH and one healthy patient in the term of the presence of the disease were used for realization of this work. Statistical analysis of the results is divided into two parts: analysis of the created algorithm based on comparison of outputs with ground truth data (manually marked references) and analysis of pathology detection on new data based on comparison of predictions with reference images from the radiologist. The proposed algorithm correctly detects present vascular pathology in 83% of cases (sensitivity) and precisely selects cases where the investigated pathology does not occur in 72% of cases (specificity). The calculated Matthews correlation coefficient is 0.53. This means that the predictive ability of the algorithm is moderate positive. The designed and developed image analysis algorithm offers the radiologist a "second opinion" and it also could enable to increase the sensitivity of CTEPH diagnostics in cooperation with a radiologist.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Lékař a technika – Clinician and Technology

ISSN

0301-5491

e-ISSN

2336-5552

Volume of the periodical

52

Issue of the periodical within the volume

2

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

8

Pages from-to

55-62

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85178909317