Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline.

ABSTRACT: Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmentation, is useful for not only measuring tissue volume and biomarkers but also for monitoring interventions. Recently, segmentation algorithms using deep learning have been widely used to reduce time humans spend to label CT data. However, the computerized segmentation has two major difficulties: managing intermediate results (e.g., resampled scans, 2D sliced image for deep learning), and setting up the system environments and packages for autonomous execution. To overcome these issues, we propose an automated pipeline for the abdominal spleen segmentation. This pipeline provides an end-to-end synthesized process that allows users to avoid installing any packages and to deal with the intermedi

SUBMITTER: Moon H 

PROVIDER: S-EPMC7086455 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature