Elsevier

Medical Engineering & Physics

Volume 51, January 2018, Pages 6-16
Medical Engineering & Physics

CT image segmentation methods for bone used in medical additive manufacturing

https://doi.org/10.1016/j.medengphy.2017.10.008Get rights and content

Highlights

  • A plethora of different CT image segmentation methods are currently used for bone.

  • The accuracy of these image segmentation methods differs markedly.

  • Global thresholding remains most common in medical additive manufacturing.

  • The accuracy and costs of medical additive manufactured constructs can be improved.

  • Future research should focus on the development of segmentation methods using CNNs.

Abstract

Aim of the study

The accuracy of additive manufactured medical constructs is limited by errors introduced during image segmentation. The aim of this study was to review the existing literature on different image segmentation methods used in medical additive manufacturing.

Methods

Thirty-two publications that reported on the accuracy of bone segmentation based on computed tomography images were identified using PubMed, ScienceDirect, Scopus, and Google Scholar. The advantages and disadvantages of the different segmentation methods used in these studies were evaluated and reported accuracies were compared.

Results

The spread between the reported accuracies was large (0.04 mm – 1.9 mm). Global thresholding was the most commonly used segmentation method with accuracies under 0.6 mm. The disadvantage of this method is the extensive manual post-processing required. Advanced thresholding methods could improve the accuracy to under 0.38 mm. However, such methods are currently not included in commercial software packages. Statistical shape model methods resulted in accuracies from 0.25 mm to 1.9 mm but are only suitable for anatomical structures with moderate anatomical variations.

Conclusions

Thresholding remains the most widely used segmentation method in medical additive manufacturing. To improve the accuracy and reduce the costs of patient-specific additive manufactured constructs, more advanced segmentation methods are required.

Introduction

Additive manufacturing (AM), also referred to as three-dimensional (3D) printing, is becoming increasingly popular in medicine [1] since it offers the possibility to personalize patient care [2]. The use of AM anatomical models results in more precise treatment planning, better communication [3], [4], and improved training and education [5], [6]. Furthermore, AM can be used for the fabrication of drill guides [7], saw guides [8], and patient-specific implants [9]. To date, medical AM is most commonly used in branches of surgery involving the musculoskeletal system, such as oral and maxillofacial surgery, traumatology, and orthopaedic surgery. However, it must be noted that the overall accuracy and repeatability of medical AM constructs used for bone reconstruction still need to be improved [10]. In this context, a recent systematic review by Martelli et al. identified 34 studies (21.5%) that reported on the unsatisfactory accuracy of medical AM constructs [11].

The current medical AM process used for the reconstruction of the musculoskeletal system can be divided into four basic steps: imaging (1); image processing (2), optionally followed by computer-aided design (3); and additive manufacturing (4) [12]. Each of these steps can introduce geometric deviations that can cause distortions in the resulting medical AM constructs [13]. Recent studies, however, suggest that the majority of the inaccuracies are introduced during imaging (Fig. 1: step 1) and image processing (Fig. 1: step 2), rather than during the manufacturing, i.e., the 3D printing process, which is generally considered to be precise [11], [14], [15].

Step 1: Imaging

CT scanners are best suited for imaging bony structures due to their superior hard tissue contrast and spatial resolution [16]. Today, a plethora of different CT technologies are available, ranging from single, helical CT to 128-slice dual-source CT configurations. Cone-beam computed tomography (CBCT) scanners are becoming increasingly popular in orthopaedic [17] and maxillofacial surgery [18] due to their lower radiation dose and costs. Raw CT data acquired during image acquisition is commonly reconstructed as a Digital Imaging and Communications in Medicine (DICOM) file.

One major challenge faced in medical AM is the large variety of different CT image acquisition and reconstruction parameters currently available (see Fig. 1; step 1 A and B). To date, to the best of our knowledge, there are no standardized protocols available for medical AM. Image slice thickness and slice interval have been identified as the primary limiting factors for the overall accuracy of medical AM constructs [19], especially when reconstructing thin bony structures from axial plane images, such as the orbital floor [20], or where the imaging plane is nearly parallel to the bone surface to be reconstructed, such as in the tibial plateau. Moreover, imaging noise, beam hardening, patient motion, and metal artifacts can introduce inhomogeneities in the gray values of CT images.

Step 2: Image processing

The medical AM process always requires image processing: the conversion of CT images into 3D surface models (Fig. 1, step 2). Such 3D surface models can be saved in a wide range of different file formats [21]. Currently, the most commonly used file format in medical AM is standard tessellation language (STL). Although rarely used, it is theoretically possible to convert CT images into slice formats that can subsequently be used for AM, thereby skipping the STL conversion process. However, it must be noted that the computer-aided design (CAD) software packages currently available on the market for medical AM still require STL files (Fig. 1, step 3).

The conversion of DICOM datasets into 3D surface models has been identified as a major source of inaccuracies in medical AM [14], [22], [23]. The most important step in this conversion process is image segmentation (Fig. 1, step 2A) [24]. Segmentation refers to the partitioning of images into regions of interest (ROIs) that correspond to anatomical structures. At present, there are a plethora of different image segmentation methods available for bony structures [25]. It remains unclear, however, which segmentation method offers the most accurate 3D surface models. Therefore, the first aim of this study was to review the existing literature on the different CT image segmentation methods currently being used for bone segmentation in medical AM. The second aim was to evaluate the impact of the different image segmentation methods on the geometric accuracy of 3D surface models.

Section snippets

Materials and methods

Existing literature on the CT image segmentation of bony structures for medical AM applications was reviewed using the PubMed Medline literature database, ScienceDirect, Scopus, and Google Scholar. An initial database of 17,700 publications was generated using the search terms “medical AND X” + “medicine AND X”, with X representing the interchangeably used terms “additive manufacturing”, “rapid prototyping”, and “3D printing”. The acquired database was subsequently filtered using the search

Results

A total of 32 publications were included in this study (Table 1). These publications were published between 2002 and 2017. To the best of our knowledge, no papers on the accuracy of medical AM constructs were published before 2002 [29]. However, it must be noted that the first publications on medical AM date back as far as the early 1990s [30].

Discussion

To date, the most common CT image segmentation method used for bone segmentation in medical AM is global thresholding. All publications reviewed in this study that used global thresholding reported geometric accuracies under 0.62 mm ± 0.76 mm [59] (Table 2). However, the reported accuracies generally included additional extensive manual post-processing, which is often very time consuming. Furthermore, the reproducibility of manual post-processing remains a challenge, especially when suboptimal

Conclusion

This literature review revealed that a plethora of different CT image segmentation methods are currently used for bone segmentation. The accuracy of these image segmentation methods differed markedly. Global thresholding remains the most widely used CT image segmentation method in medical AM but often requires extensive manual post-processing. Advanced thresholding approaches could improve the accuracy of global thresholding, but such methods are currently not implemented in commercially

Competing interests

None declared.

Funding

None.

Ethical approval

Not required.

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