Naslov
Whole Heart Segmentation from CT images Using 3D U-Net architecture

Autori
Habijan, Marija ; Leventic, Hrvoje ; Galic, Irena ; Babin, Danilo

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

Izvornik
2019 International Conference on Systems, Signals and Image Processing (IWSSIP) / - Osijek : Institute of Electrical and Electronics Engineers (IEEE), 2019, 121-126

ISBN
978-1-7281-3253-2

Skup
26th International Conference on Systems, Signals and Image Processing (IWSSIP 2019)

Mjesto i datum
Osijek, Hrvatska, 05.06.2019. - 07.06.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
CT ; data augmentation ; heart segmentation ; medical image segmentation ; neural networks ; volumetric segmentation

Sažetak
Recent studies have demonstrated the importance of neural networks in medical image processing and analysis. However, their great efficiency in segmentation tasks is highly dependent on the amount of training data. When these networks are used on small datasets, the process of data augmentation can be very significant. We propose a convolutional neural network approach for the whole heart segmentation which is based upon the 3D U-Net architecture and incorporates principle component analysis as an additional data augmentation technique. The network is trained end-to-end i.e. no pre-trained network is required. Evaluation of the proposed approach is performed on 20 3D CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, divided into 15 training and 5 validation images. Final segmentation results show a high Dice coefficient overlap to ground truth, indicating that the proposed approach is competitive to state-of-the-art Additionally, we provide the discussion of the influence of different learning rates on the final segmentation results.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA