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Pregled bibliografske jedinice broj: 1139233

Automatic extraction of multiple-study X-ray images


Dumenčić, Stella; Tschauner, Sebastian; Hržić, Franko; Štajduhar, Ivan
Automatic extraction of multiple-study X-ray images // 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
Köseköy, Turska, 2021. 87, 6 doi:10.1109/INISTA52262.2021.9548551 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1139233 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Automatic extraction of multiple-study X-ray images

Autori
Dumenčić, Stella ; Tschauner, Sebastian ; Hržić, Franko ; Štajduhar, Ivan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) / - , 2021

ISBN
978-1-6654-3603-8

Skup
International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

Mjesto i datum
Köseköy, Turska, 25.08.2021. - 27.08.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
object detection ; YOLO ; radiograph ; image segmentation

Sažetak
In medical radiology standard practice, radiographs of different projections or studies are occasionally merged into a single image for convenience. Before the radiographs can be utilized for further automatic processing, e.g., for detecting and/or localizing specific injuries, such merged projections or studies need to be separated. Doing this manually can be tiresome ; therefore, we decided to investigate the possibility of automating this process using contemporary image processing and machine learning techniques. We implemented two independent automatic solutions for this problem: (1) a manually-tailored image-splitting method using morphological operations, image binarization, and boundary-box detection, and (2) an instance of the YOLOv4 algorithm. The manually-tailored algorithm and YOLOv4 model were trained on the training data set of 4, 000 images and validated on the data set of 250 images. Comparing both on a disjoint test set, consisting of 250 images, the YOLOv4 model noticeably outperformed the manually-tailored method with an accuracy of 0.992, F1 score of 0.996, and the intersection-overunion of 0.8978±0.079. The results suggest that YOLOv4 can efficiently extract large portions of embedded projections or studies from medical radiographs.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Kliničke medicinske znanosti



POVEZANOST RADA


Projekti:
HRZZ-IP-2020-02-3770 - Strojno učenje za prijenos znanja u medicinskoj radiologiji (RadiologyNET) (Štajduhar, Ivan, HRZZ - 2020-02) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-15 - Razvoj postupaka temeljenih na strojnom učenju za prepoznavanje bolesti i ozljeda iz medicinskih slika (Štajduhar, Ivan, NadSve ) ( CroRIS)

Ustanove:
Tehnički fakultet, Rijeka

Profili:

Avatar Url Franko Hržić (autor)

Avatar Url Ivan Štajduhar (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Dumenčić, Stella; Tschauner, Sebastian; Hržić, Franko; Štajduhar, Ivan
Automatic extraction of multiple-study X-ray images // 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)
Köseköy, Turska, 2021. 87, 6 doi:10.1109/INISTA52262.2021.9548551 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Dumenčić, S., Tschauner, S., Hržić, F. & Štajduhar, I. (2021) Automatic extraction of multiple-study X-ray images. U: 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) doi:10.1109/INISTA52262.2021.9548551.
@article{article, author = {Dumen\v{c}i\'{c}, Stella and Tschauner, Sebastian and Hr\v{z}i\'{c}, Franko and \v{S}tajduhar, Ivan}, year = {2021}, pages = {6}, DOI = {10.1109/INISTA52262.2021.9548551}, chapter = {87}, keywords = {object detection, YOLO, radiograph, image segmentation}, doi = {10.1109/INISTA52262.2021.9548551}, isbn = {978-1-6654-3603-8}, title = {Automatic extraction of multiple-study X-ray images}, keyword = {object detection, YOLO, radiograph, image segmentation}, publisherplace = {K\"{o}sek\"{o}y, Turska}, chapternumber = {87} }
@article{article, author = {Dumen\v{c}i\'{c}, Stella and Tschauner, Sebastian and Hr\v{z}i\'{c}, Franko and \v{S}tajduhar, Ivan}, year = {2021}, pages = {6}, DOI = {10.1109/INISTA52262.2021.9548551}, chapter = {87}, keywords = {object detection, YOLO, radiograph, image segmentation}, doi = {10.1109/INISTA52262.2021.9548551}, isbn = {978-1-6654-3603-8}, title = {Automatic extraction of multiple-study X-ray images}, keyword = {object detection, YOLO, radiograph, image segmentation}, publisherplace = {K\"{o}sek\"{o}y, Turska}, chapternumber = {87} }

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