Hoang, Hong Son and Pham, Cam Phuong and Franklin, Daniel and Walsum, Theo van and Luu, Manh Ha (2019) An Evaluation of CNN-based Liver Segmentation Methods using Multi-types of CT Abdominal Images from Multiple Medical Centers. In: 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 25-27/09/2019, Ho Chi Minh city.
Full text not available from this repository.Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Electronics and Communications > Electronics and Computer Engineering |
| Divisions: | Advanced Insitute of Engineering and Technology (AVITECH) Faculty of Electronics and Telecommunications (FET) |
| Depositing User: | Lưu Mạnh Hà |
| Date Deposited: | 28 Nov 2019 08:59 |
| Last Modified: | 28 Nov 2019 08:59 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/3658 |
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