Journal
SENSORS
Volume 21, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s21093249
Keywords
deep convolutional neural networks; medical image segmentation; structure information; domain robustness
Funding
- National Research Foundation of Korea(NRF) - Ministry of Education [NRF-2018R1D1A1A02086017]
- NRF - Ministry of Education [NRF-2019R1A6A1A03032119]
Ask authors/readers for more resources
This paper proposes a method to enhance the performance of segmentation models for medical images by jointly learning the global structure information. Experimental results demonstrate that the proposed method not only improves segmentation performance but also enhances robustness against domain shifts.
In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available