Data annotation is often a prerequisite for applying deep learning to medical image segmentation. It is a tedious process that requires substantial guidance from experienced physicians. Adipose tissue labeling on CT scans is particularly time-consuming because adipose tissue is present throughout the entire body. One possible solution is to create inaccurate annotations from conventional (non-deep learning) adipose tissue segmentation methods. This work demonstrates the development of a deep learning model directly from these inaccurate annotations. The model is a multi-scale 3D residual U-Net where the encoder path is composed of residual blocks and the decoder path fuses multi-scale feature maps from different layers of decoder blocks. The training set consisted of 101 patients and the testing set consisted of 14 patients. Ten patients with anasarca were purposely added to the testing dataset as a stress test to evaluate model generality. Anasarca is a medical condition that leads to the generalized accumulation of edema within subcutaneous adipose tissue. Edema creates heterogeneity inside the adipose tissue which is absent in the training data. In comparison with a baseline method of manual annotations, the Dice coefficient improved significantly from 73.4 ± 14.1% to 80.2 ± 7.1% (p < 0.05). The model trained on inaccurate annotations improved the accuracy of adipose tissue segmentation by 7% without the need for any manual annotation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.