## 4 Discussion
Higher spatial resolutions possible via CT also improve the accuracy and effectiveness of automatic and semi-automatic segmentation processes, better supporting 3 DPLM production efficiency [10; 20]. Segmentation involves digitally extracting the individual voxels within a patient's imaging volume, which correspond to the liver parenchyma and separate intrahepatic structures and lesions [12; 50]. Segmentations are exported in a file format--typically stereolithography (STL)--that uses a tessellated polygon mesh to represent the 3D hepatic structures' surface geometries [12; 14; 15; 16; 22; 34; 35]. The 3D printer uses these files to produce a 3D-printed model [12]. Figure 5 demonstrates an example of a final STL rendering used for 3 DPLM production, compared to a segmented CT image slice and original CT image slice from the corresponding patient imaging data. Figure 4: Comparison of four different 3 DPLM construction approaches. (1) Single/large-piece 3D-printed parenchyma/tumour model; (2) 3D-printed intrahepatic structures model (with/without 3D-printed parenchyma casing); (3) 3D-printed intrahepatic structures model and parenchyma mould with casted/transparent parenchyma; (4) simulation-specific hollow models. Image 1 (**top left**) reprinted with permission under the open access from [PERSON] et al. [32]. Image 2 (**top right**) reprinted with permission under the open access from [PERSON] et al. [29]. Image 3 (**bottom left**) reprinted with permission under the open access from [PERSON] et al. [15]. Image 4 (**bottom right**) reprinted with permission under the open access from [PERSON] et al. [37]. The current 'gold standard' for diagnostic-use liver segmentation is a manual approach (i.e., voxel-by-voxel, slice-by-slice); however, this is highly laborious and time consuming [20, 51]. Accordingly, the articles in this review use combinations of semi-automatic and automatic segmentation techniques, including adaptive region growing [13, 17, 31, 32, 35] and threshold filtering [17, 32] (i.e., region-based techniques [50]); and active contour algorithms [13, 31] (i.e., boundary-based techniques [50]). These algorithm-based approaches automatically isolate and segment liver/intrahepatic tissues based on homogeneities and/or changes in local voxel/tissue intensity [50]. They increase segmentation efficiency considerably, but still require time-consuming manual validation and correction [11, 16, 17, 35, 51, 52, 53]. A major limitation of 3 DPLMs is their time and monetary costs [9, 11, 13, 14, 15, 16, 17, 19, 31, 35, 36]. Average 3 DPLM production times across the reviewed articles were \(\sim\)24 h, owing to the laborious nature of segmentation and wait times during the 3 DP process [14, 15, 35]. Ongoing research aims to increase the efficiency and reproducibility of automatic liver segmentation using artificial intelligence to reduce 3 DPLM production times [12]. The average cost of 3 DPLMs in this review ranged from \(\sim\)USD 100 to \(\sim\)USD 2000 (excludes software and 3D printer costs). Costs should decrease as 3 DP technology becomes more accessible; however, they currently limit the 3 DPLMs' feasibility for day-to-day clinical use [16, 19, 31, 33, 35, 40]. 3 DPLMs' clinical feasibility must be considered against their 3 DP technology [22]. For example, an FFF 3 DPLM's resolution is limited by the FFF printer's nozzle diameter [21]. Lesions and vessels smaller than this diameter cannot be printed [21]. It is possible to artificially increase the diameter of lesions that are otherwise too small to print, as demonstrated by [PERSON] et al. [34]; however, this approach undermines dimensional accuracy. It could not be used for a 3 DPLM used to guide a millimetre-precise LCR--instead, a more advanced, and likely more expensive, 3D printer technology would be required. In contrast, a patient education 3 DPLM could sacrifice millimetre accuracy to reduce costs.