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

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.

Figure 1.

A graphical history of publications on the topic of additive manufacturing in cardiovascular applications. Note that data for 2016 are only correct up to 27 January 2016.

Figure 2.

A flow diagram summarising the identification, screening, retrieval, eligibility and inclusion of records and full text resources within the systematic review.

Figure 3.

A summary of the different imaging modalities used to acquire data from which 3D models can be developed. Values represent the fraction of journal publications (left) and conference, technical and case reports (right) that use each modality. Note that as a single publication can report the use of more than one modality, the fraction of publications using each method need not sum to 1. CT: x-ray computed tomography; CTA: x-ray computed tomography angiogram, MRI: electrocardiogram- (ECG) and / or respiratory-navigated balanced steady state free precession; MRA: contrast-enhanced magnetic resonance angiogram; PC: phase contrast magnetic resonance imaging, US: ultrasound, Echo: echocardiogram.

Figure 4.

A summary of the SDQ and segmentation method data extracted from the journal publications (both reviews and articles) included in the review. The top pie breaks down the SDQ score characteristics of the 80 publications. The methods used within publications with SDQ = 2 or 3 are then summarised in the two lower pies. Note that as a single publication can report the use of more than one method, the fraction of publications using each method need not add up to 1.