A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system

N Byrne; M Velasco Forte; A Tandon; I Valverde; T Hussain

doi:10.1177/2048004016645467

A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system

JRSM Cardiovasc Dis. 2016 Apr 29:5:2048004016645467. doi: 10.1177/2048004016645467. eCollection 2016 Jan-Dec.

Authors

N Byrne¹, M Velasco Forte², A Tandon³, I Valverde⁴, T Hussain⁵

Affiliations

¹ Department of Medical Physics, Guy's and St. Thomas' NHS Foundation Trust, London, UK; Paediatric Cardiology, Evelina London Children's Hospital at Guy's and St. Thomas' NHS Foundation Trust, London, UK; Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
² Paediatric Cardiology, Evelina London Children's Hospital at Guy's and St. Thomas' NHS Foundation Trust, London, UK; Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
³ Departments of Paediatrics, University of Texas, Southwestern Medical Center, Dallas, TX, USA.
⁴ Paediatric Cardiology, Evelina London Children's Hospital at Guy's and St. Thomas' NHS Foundation Trust, London, UK; Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Department of Paediatric Cardiology, Hospital Virgen del Rocio, Seville, Spain; Institute of Biomedicine of Seville, Seville, Spain.
⁵ Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Departments of Paediatrics, University of Texas, Southwestern Medical Center, Dallas, TX, USA.

Abstract

Background: Shortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes.

Methods: A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ) and segmentation software were recorded.

Results: Totally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports). The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992-2015). The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced.

Conclusions and implication of key findings: Predominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods.

Keywords: 3D printing; Computed tomography and magnetic resonance imaging; cardiovascular surgery; diagnostic testing; image segmentation; paediatric and congenital heart disease.