The performance and evaluation of segmentation algorithms will benefit from large fully annotated data sets, but the heavy workload of manual contouring is unrealistic in clinical and research practice. In this work, we propose a method of automatically creating pseudo ground truth (p-GT) segmentations of anatomical objects from given sparse manually annotated slices and utilize them to evaluate actual segmentations. Sparse slices are selected spatially evenly on the whole slice range of the target object, where one slice is selected to conduct manual annotation and the next t slices are skipped, repeating this process starting from one end of the object to its other end. A shape-based interpolation (SI) strategy and an object-specific 2D U-net based deep learning (DL) strategy are investigated to create p-GT. The largest t value where the created p-GT is considered to be not statistically significantly different from the actual ground with its natural imprecision due to variability in manually specified ground truth is determined as the optimal t for the considered object. Experiments are conducted on ~300 computed tomography (CT) studies involving two objects – cervical esophagus and mandible and two segmentation evaluation metrics – Dice Coefficient and average symmetric boundary distance. Results show that the DL strategy overwhelmingly outperforms the SI strategy, where ~95% and ~66-83% of manual workload can be reduced without sacrificing evaluation accuracy compared to actual ground truth data via the DL and SI strategies respectively. Furthermore, the p-GT with optimal t is able to evaluate actual segmentations with accurate metric values.
In the visualization of three-dimensional (3D) images, specific isosurfaces are usually extracted from 3D images and used to represent (approximate) boundary surfaces of certain structures within 3D images. In order to well approximate the boundary surfaces of these structures, it is important to determine a good isosurface for each boundary surface. An isosurface is said to be a good isosurface of a boundary surface if it can approximate the boundary surface with the smallest error under certain error measuring criteria. The mathematical model describing the approximation problem of a boundary surface by isosurfaces is constructed and studied. The method used to deduce good isosurfaces for the boundary surfaces within 3D discrete images is presented. The proposed method is illustrated by examples with different real 3D biomedical images.
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.