Exploring the Role of Deep Learning in Liver Segmentation
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. |
| Pages | 839-845 |
| ISBN (electronic) | 978-1-6654-9079-5 |
| ISBN (print) | 978-1-6654-9080-1 |
| Publication status | Published - 2023 |
Publication series
| Name | Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA |
|---|
Conference
| Title | 3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA 2023) |
|---|---|
| Place | China |
| City | Chongqing |
| Period | 26 - 28 May 2023 |
Link(s)
Abstract
Delineating a target organ is the procedure known as biomedical segmentation. Liver cancer is the second most common cancer to cause death. The automatic liver segmentation from CT scans is a necessary job for the diagnosis of liver cancer disease. However, because of its irregular form and proximity to other organs, the liver is one of the more challenging organs to segment. For liver segmentation in the current research, datasets with only liver labels are typically used, the model is not resistant to cases involving multiple phases and vendors. AbdomenCT1K is a large and various abdominal CT datasets from 12 medical institutions, including cases with multiple diseases, vendors, and phases. To segment the liver, we use the Dense-UNet trained with AbdomenCT-1K, and it achieved the dice of 0.9271 and NSD of 0.9685. We compare different backbone networks in this research, and the results show that our model is more reliable and performs better than many other deep learning models in the prior study. © 2023 IEEE.
Research Area(s)
- AbdomenCT-1K, Dense-UNet, image preprocessing, liver segmentation
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Exploring the Role of Deep Learning in Liver Segmentation. / Wang, Kuanglan; Chan, Kwan Ho; Liu, Engui et al.
2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 839-845 (Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA).
2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). Institute of Electrical and Electronics Engineers, Inc., 2023. p. 839-845 (Proceedings of IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
