Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.

ABSTRACT:

Purpose

The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing method that uses the segmented images to correct for the low-frequency shading artifacts in reconstructed images.

Methods

A sequential and iterative application of image segmentation and low-order polynomial fitting to bCT volume data sets was used in the interleaved correction (IC) method. Image segmentation was performed through a deep convolutional neural network (CNN) with a modified U-Net architecture. A total of 45 621 coronal bCT images from 111 patient volume data sets were segmented (using a previously published segmentation

SUBMITTER: Ghazi P 

PROVIDER: S-EPMC8761082 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature