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Pregled bibliografske jedinice broj: 1155097

Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach


Baressi Šegota, Sandi; Lorencin, Ivan; Smolić, Klara; Anđelić, Nikola; Markić, Dean; Mrzljak, Vedran; Štifanić, Daniel; Musulin, Jelena; Španjol, Josip; Car, Zlatan
Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach // Biology, 10 (2021), 11; 1134, 25 doi:10.3390/biology10111134 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1155097 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach

Autori
Baressi Šegota, Sandi ; Lorencin, Ivan ; Smolić, Klara ; Anđelić, Nikola ; Markić, Dean ; Mrzljak, Vedran ; Štifanić, Daniel ; Musulin, Jelena ; Španjol, Josip ; Car, Zlatan

Izvornik
Biology (2079-7737) 10 (2021), 11; 1134, 25

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial intelligence ; computer tomography ; machine learning ; semantic segmentation ; urinary bladder cancer

Sažetak
Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro of 0.9999 and s(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC up to 0.9587 and s(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC up to 0.9372 and s(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC values up to 0.9660 with a s(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Kliničke medicinske znanosti, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)



POVEZANOST RADA


Projekti:
EK-KF-KK.01.1.1.01.0009-2 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima - IJ za napredne kooperativne sustave (DATACROSS) (Petrović, Ivan; Šmuc, Tomislav, EK - KK.01.1.1.01) ( POIROT)
EK-EFRR-KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša, EK - KK.01.2.2.03) ( POIROT)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( POIROT)
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( POIROT)

Ustanove:
Medicinski fakultet, Rijeka,
Tehnički fakultet, Rijeka,
Klinički bolnički centar Rijeka

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.mdpi.com

Citiraj ovu publikaciju:

Baressi Šegota, Sandi; Lorencin, Ivan; Smolić, Klara; Anđelić, Nikola; Markić, Dean; Mrzljak, Vedran; Štifanić, Daniel; Musulin, Jelena; Španjol, Josip; Car, Zlatan
Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach // Biology, 10 (2021), 11; 1134, 25 doi:10.3390/biology10111134 (međunarodna recenzija, članak, znanstveni)
Baressi Šegota, S., Lorencin, I., Smolić, K., Anđelić, N., Markić, D., Mrzljak, V., Štifanić, D., Musulin, J., Španjol, J. & Car, Z. (2021) Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach. Biology, 10 (11), 1134, 25 doi:10.3390/biology10111134.
@article{article, author = {Baressi \v{S}egota, Sandi and Lorencin, Ivan and Smoli\'{c}, Klara and An\djeli\'{c}, Nikola and Marki\'{c}, Dean and Mrzljak, Vedran and \v{S}tifani\'{c}, Daniel and Musulin, Jelena and \v{S}panjol, Josip and Car, Zlatan}, year = {2021}, pages = {25}, DOI = {10.3390/biology10111134}, chapter = {1134}, keywords = {artificial intelligence, computer tomography, machine learning, semantic segmentation, urinary bladder cancer}, journal = {Biology}, doi = {10.3390/biology10111134}, volume = {10}, number = {11}, issn = {2079-7737}, title = {Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach}, keyword = {artificial intelligence, computer tomography, machine learning, semantic segmentation, urinary bladder cancer}, chapternumber = {1134} }
@article{article, author = {Baressi \v{S}egota, Sandi and Lorencin, Ivan and Smoli\'{c}, Klara and An\djeli\'{c}, Nikola and Marki\'{c}, Dean and Mrzljak, Vedran and \v{S}tifani\'{c}, Daniel and Musulin, Jelena and \v{S}panjol, Josip and Car, Zlatan}, year = {2021}, pages = {25}, DOI = {10.3390/biology10111134}, chapter = {1134}, keywords = {artificial intelligence, computer tomography, machine learning, semantic segmentation, urinary bladder cancer}, journal = {Biology}, doi = {10.3390/biology10111134}, volume = {10}, number = {11}, issn = {2079-7737}, title = {Semantic Segmentation of Urinary Bladder Cancer Masses From CT Images: A Transfer Learning Approach}, keyword = {artificial intelligence, computer tomography, machine learning, semantic segmentation, urinary bladder cancer}, chapternumber = {1134} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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