Liver Segmentation: A Capsule Based Network
Youssef Ouassit, Soufiane Ardchir, Mohammed Yassine El Ghoumari, Mohamed Azzouazi
Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
31 January 2020 |
15 February 2020 |
10 March 2020 |
Abstract: Liver segmentation is essential in many clinical applications, such as pathological diagnosis of liver disease, visualization, etc. However, segmentation of the liver is still a very difficult task due to the complex context, fuzzy and blurred boundaries between abdominal and the low contrast. Deep models have been very successful in many tasks such as detection, classification and segmentation on non-medical images due to the insufficient availability of labeled data. In the medical field, labeled data is still limited due to privacy concerns and the need for experts. In this paper, we present a new automatic and efficient model based on capsules network. Our proposed network is an end-to-end learning process that can eliminate redundant computations and reduce the risk of overfitting. The model has been validated on 3D liver segmentation CHAOS challenges and have obtained competitive segmentation results compared to state-of-the-art on such a limited amount of labeled data
