Fully automated organ segmentation on Computed Tomography (CT) images is an important first step in many medical applications. Many different Deep Learning (DL) based approaches are being actively developed for this task. However, often it is hard to make a direct comparison between two segmentation methods. We tested the performance of two deep learning-based CT on an independent dataset of CT scans. Algorithm-1 performed much better on the segmentation of the kidney. In contrast, the performance of the two algorithms was similar for the segmentation of the liver. For both algorithms, a number of outliers (Dice <= 0.5) were observed. With limited scan acquisition parameters, it was not possible to diagnose the root cause for the outliers. This work highlights the urgent need for complete DICOM header curation. The DICOM header information could help to pin-point the scanning parameters that lead to segmentation errors by Deep Learning algorithms.
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.