Pregled bibliografske jedinice broj: 1077453
Transfer Learning with U-Net type model for Automatic Segmentation of Three Retinal Layers In Optical Coherence Tomography Images
Transfer Learning with U-Net type model for Automatic Segmentation of Three Retinal Layers In Optical Coherence Tomography Images // Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis
Dubrovnik, Hrvatska, 2019. str. 49-53 doi:10.1109/ISPA.2019.8868639 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Transfer Learning with U-Net type model for
Automatic Segmentation of Three Retinal Layers
In Optical Coherence Tomography Images
Autori
Zadro Matovinović, Ivana ; Lončarić Sven ; Lo, Julian ; Heisler, Morgan ; Sarunic, Marinko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis
/ - , 2019, 49-53
Skup
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
Mjesto i datum
Dubrovnik, Hrvatska, 23.09.2019. - 25.09.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Age Related Macular Degeneration , OCT , Retinal Layer Segmentation , Transfer Learning , U-Net , ResNet34
Sažetak
Retinal layer analysis on OCT images is a standard procedure used by ophthalmologists to diagnose various diseases. Due to a large number of generated OCT images for each patient, a manual image analysis can be time-consuming and error-prone, which can consequently affect the timeliness and quality of the diagnosis. Therefore, in recent years, a variety of methods, based prevalently on deep learning, have been proposed for the automatic segmentation of retinal layers. In our study, the U-Net type model with a ResNet based encoder, pretrained on ImageNet dataset is utilized. In addition, the model is combined with postprocessing step to obtain segmented layer boundaries. The modified versions of U-Net type model have already been applied to various non-medical imaging segmentation tasks, achieving outstanding results. To investigate whether the pretrained U-Net type model contributes to improvement of retinal layer segmentation, two models are trained and validated on 23 volumes of OCT images with age related macular degeneration (AMD): the U-Net model with pretrained ResNet34 encoder on ImageNet dataset and the original U-Net model, trained from the scratch. The one-sided Wilcoxon signed-rank test has shown that the pretrained U-Net type model outperforms the original U-Net model for segmenting three regions bounded by four layer boundaries.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
