A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging.

ABSTRACT:

Purpose

Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work aimed to develop a fast, prior knowledge-guided DL semiautomatic segmentation (DL-SAS) method for complex structures on abdominal magnetic resonance imaging (MRI) scans.

Methods and materials

A novel application using contours on an adjacent slice as a prior knowledge informant in a 2-dimensional UNet DL model to guide autosegmentation for a subsequent slice was implemented for DL-SAS. A generalized, instead of organ-specific, DL-SAS model was trained and tested for abdominal organs on T2-weighted MRI scans collected from 75 pati

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9639200 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature