TY  - CONF
ID  - SisLab3658
UR  - https://ieeexplore.ieee.org/document/8905166
A1  - Hoang, Hong Son
A1  - Pham, Cam Phuong
A1  - Franklin, Daniel
A1  - Walsum, Theo van
A1  - Luu, Manh Ha
Y1  - 2019/09/25/
N2  - Automatic segmentation of CT images has recently
been applied in several clinical liver applications. Convolutional Neural Networks (CNNs) have shown their effectiveness in medical image segmentation in general and also in liver segmentation. However, liver image quality may vary between medical centers due to differences in the use of CT scanners, protocols, radiation dose, and contrast enhancement. In this paper, we investigate three wells known CNNs, FCN-CRF, DRIU, and V-net, for liver segmentation using data from several medical centers. We perform qualitative evaluation of the CNNs based on Dice score, Hausdorff distance, mean surface distance and false positive rate. The results show that all three CNNs achieved a mean Dice score of over 90% in liver segmentation with typical contrast
enhanced CT images of the liver. p-values from paired T-test on Dice score of the three networks using Mayo dataset are larger than 0.05 suggesting that no statistical significant difference in their performance. DRIU performs the best in term of processing time. The results also demonstrate that those CNNs have reduced performance in liver segmentation in the case of low-dose and non-contrast enhanced CT images. In conclusion, these promising results enable further investigation of alternative deep learning based approaches to liver segmentation using CT images.
TI  - An Evaluation of CNN-based Liver Segmentation
Methods using Multi-types of CT Abdominal
Images from Multiple Medical Centers
M2  - Ho Chi Minh city
AV  - none
T2  - 2019 19th International Symposium on Communications and Information Technologies (ISCIT)
ER  -