Smoothing Module for Optimization Cranium Segmentation Using 3D Slicer

Anatomy is the most essential course in health and medical education to study parts of human body and also the function of it. Cadaver is a media used by medical student to study anatomical subject. Because of limited access to cadaver and also due to high prices, this situation makes it necessary to develope an alternative anatomical education media, one of them is the use 3D printing to produce anatomical models. Before 3D Print the cranium, it is necessary to do the segmentation process and often the segmentation result is not good enough and appear a lot of noises. The purpose of this research is to optimize a 3D cranium based on DICOM (digital imaging and communications in medicine) data processing using the smoothing modules on 3D Slicer. The method of this research is to process the Cranium DICOM data using 3D Slicer software by varying the 5 types of smoothing modules. The results with default parameter fill holes and median have better results compared to others. Kernel size variations are performed for smoothing module fill holes and medians. The result is fill holes get optimal segmentation results using a kernel size of 3 mm and the median is 5 mm


Introduction
Anatomical education is one of the most important courses to learn in the health and medical field [1]. Anatomy is studied using a body donor called cadavers. Cadavers can be obtained in several ways, such as direct donations from people who have agreed that they will donate their dead bodies to the university or the bodies of patients who died in hospitals but no family claiming their bodies [2].
The cadavers are high in price and the number of cadavers in university is limited [3]. Universitas Gadjah Mada (UGM) hasn't received any cadavers since 2001. These conditions are the reason for the development of anatomical education media, one of them is the use of 3-dimensional (3D) anatomical models.
Pandemic condition was also the reason for the development of anatomical education media using 3D technology due to the limited access to campus and the lab. 3D anatomical models provide an overview of anatomical structures more practically and are very useful for explaining some of the functions and relationships between anatomical structures. Anatomical education media using 3D models can reduce costs at the institution which is the costs for cadaver storage.
Because of this condition the development of medical advancement and 3D of anatomical model is the one of priority in this modern era [4]. One that is often done is multi modality 3D imaging technology and analysis of DICOM data using computer science and bio informatics. The purpose of this advance development is the medical workers can quickly see the visual of the patient body performed using CT or MRI then the results can be viewed in 3D and medical workers can diagnose patients. 3D imaging can also be used as a reference if the patient has defects or various medical disorders, which is important information in the medical field as a way to plan or determine procedures such as surgery that must be performed on patients [5]. Slicer that can perform medical image processing and visualization from DICOM Data and can be exported to 3D model mesh data such as STL and OBJ format [6]. 3D slicers are often used as a platform for developing and analyzing to produce prototype 3D models that suit clinical needs [7]. The result of prototype can be a 3D model or can be 3D printed to create a physical prototype of anatomical model.

International Journal of Applied Sciences and Smart Technologies
In this reserach, we use 3D Slicer as a software for prototyping and development of cranium image analysis tools for clinical research applications and also provide an optimization using smoothing modules to make an optimal 3D model of anatomical model such as cranium that used in this research.

Methods
This research only using cranium as anatomical model and the method to segmentation the cranium data is accordance with research that has been done previously using the threshold technique [8]. • DICOM Viewer and segmentation The DICOM file was processed using the DICOM Viewer and Segmentation software that is 3D Slicer. The software was used to view the DICOM file and segment it to obtain a region of interest for this research, the cranium. Threshold value that used in this research is 150 for the lower limit and the upper limit using the default value 2976. After the segmentation process, then the next step is to vary the smoothing module with the aim of eliminating some areas that are not included in the region of interest or can also be referred to as noise.

• Smoothing Modules
The smoothing module is located in the segment editor which main function is to smooth the area that has been segmented before. There are 5 types of smoothing modules that are median, opening, closing, gaussian, and joint smoothing [9]. Median is to removes small extrusions and fills small gaps while keeps smooth contours mostly unchanged. Applied to selected segment only. Opening is to removes object smaller than the specified kernel size. Closing is fills sharp corner and hole smaller than the specified kernel size. Gaussian has stronger smoothing for all object details, but tend to shrink the object. Joint smoothing is tend to preserving watertight between separate objects. After vary the 5 types of smoothing modules, 2 modules will be selected for the next step which is vary the kernel size to know the effect on the result of cranium segmentations. Kernel size is diameter of neighborhood that will considered around each voxel. In the 3D Slicer software documentation, it is explained that the greater the kernel size value, will makes the smoothing stronger and more details will be surpressed. In this study will vary 5 values of kernel size, that is 1, 2, 3, 4, and 5 mm with 3 mm is the default kernel size.

Results and Discussions
This research used 3D Slicer software for segmentation the cranium from DICOM data.
This step used the same threshold value.  After opening the DICOM file, segmentation was carried out to determine the region of interest.
The segmentations result showed in Fig. 3 is segmentation without using smoothing module. Next will be discussed the results of segmentation using smoothing modules which will be shown in 2 views that is coronal plane and sagittal plane.   Fig. 6. If using fill holes, the result is that many cavities in a bone become more closed. In addition, there are also many parts of the cranium that are connected better using this module and can be seen around the occipital and sphenoid have quite good detailed results. If it use the median smoothing module that can be seen in Fig. 7, the result is almost similar to fill holes, which close several cavities in the bone and can join several separate parts if segmented without using a smoothing module. But in certain parts there are several parts disconnected such as around the frontal and sphenoid.

International Journal of Applied Sciences and Smart Technologies
As a decision in this first step of experiment, smoothing module fill holes and medians will be selected to proceed to the second experimental step, which is to vary The results of cranium segmentation using smoothing module fill holes by varying the value of the kernel size can be seen in Fig. 8 for coronal view and Fig. 9 for sagittal view. When viewed from the coronal view for kernel size variations does not provide a significant difference.    The results of segmentation using the smoothing module median by varying the kernel size can be seen in Fig. 10 for coronal view and Fig. 11 for sagittal view. The coronal view median has a slight difference with fill holes, which is at the kernel size of   kernel size other than 3 mm also has other similarities that can be seen in Fig.   13, which both have details around the maxilla that are not found in the kernel size of 3 mm. Therefore, for the most optimal results 5 mm kernel size was chosen even though the results were almost the same as 1 mm, 2 mm, and 4 mm, but the 5 mm kernel size subjectively had more optimal results compared to others.

Conclusions
Studies on the effect of smoothing modules and kernel size have been presented on the results of cranium segmentation. The results of this study used 5 types of smoothing modules fill holes, gaussian, join smoothing, median, and opening smoothing. The results with default parameter fill holes and median have better results compared to others. Kernel size variations are performed for smoothing module fill holes and medians. The result is fill holes get optimal segmentation results using a kernel size of 3 mm and the median is 5 mm. This research is limited to its application to cranium segmentation only. The direction of future research is to conduct a study of the optimal smoothing module for each anatomy part such as the brain, heart, liver, and abdomen.