NnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images.

ABSTRACT:

Background

Recipient donor matching in liver transplantation can require precise estimations of liver volume. Currently utilized demographic-based organ volume estimates are imprecise and nonspecific. Manual image organ annotation from medical imaging is effective; however, this process is cumbersome, often taking an undesirable length of time to complete. Additionally, manual organ segmentation and volume measurement incurs additional direct costs to payers for either a clinician or trained technician to complete. Deep learning-based image automatic segmentation tools are well positioned to address this clinical need.

Objectives

To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical im

SUBMITTER: Pettit RW 

PROVIDER: S-EPMC9585534 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature