Feature-based automated segmentation of ablation zones by fuzzy c-mean clustering during low-dose computed tomography.

ABSTRACT:

Purpose

Intra-procedural monitoring and post-procedural follow-up is necessary for a successful ablation treatment. An imaging technique which can assess the ablation geometry accurately is beneficial to monitor and evaluate treatment. In this study, we developed an automated ablation segmentation technique for serial low-dose, noisy ablation computed tomography (CT) or contrast-enhanced CT (CECT).

Methods

Low-dose, noisy temporal CT and CECT volumes were acquired during microwave ablation on normal porcine liver (four with non-contrast CT and eight with CECT). Highly constrained backprojection (HYPR) processing was used to recover ablation zone information compromised by low-dose noise. First-order statistic features and normalized fractional Brownian features (NBF) were us

SUBMITTER: Wu PH 

PROVIDER: S-EPMC8594246 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature