CT-based True- and False-Lumen Segmentation in Type B Aortic Dissection Using Machine Learning.

ABSTRACT:

Purpose

To develop a segmentation pipeline for segmentation of aortic dissection CT angiograms into true and false lumina on multiplanar reformations (MPRs) perpendicular to the aortic centerline and derive quantitative morphologic features, specifically aortic diameter and true- or false-lumen cross-sectional area.

Materials and methods

An automated segmentation pipeline including two convolutional neural network (CNN) segmentation algorithms was developed. The algorithm derives the aortic centerline, generates MPRs orthogonal to the centerline, and segments the true and false lumina. A total of 153 CT angiograms obtained from 45 retrospectively identified patients (mean age, 50 years; range, 22-79 years) were used to train (n = 103), validate (n = 22), and te

SUBMITTER: Hahn LD 

PROVIDER: S-EPMC7977949 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature