# Autostereoscopic 3D Display System for 3D Medical Images
## 2 Methods
### Eye Tracking
The left image shows an extracted subregion (red box) including the eyes and nose of the subject. The middle image shows the 11 landmark points (green dots) used to inform the Supervised Descent Method (SDM)-based shape alignments. In the right image, the green circles around the red points on the eyes indicate the pupil segmentation modules, which increase the accuracy of the eye tracking. module revises the importance of different feature map positions and then the occluded part can be inferred by non-occluded parts. The proposed re-weight method infers the pixel confidence on the feature map using both the landmark appearance and the graphical structure between landmarks, and the network can be trained end-to-end. The proposed revised PFLD network is shown in Figure 6.  
D Autostereoscopy Visualization Feasibility for 3D Medical Imaging: 3D Cardiac CT Navigator  
We visualized 3D coronary CT angiography (CTA) images using the proposed 3D autostereoscopic display system based on the eye tracking algorithm. In collaboration with SMC medical doctors, we developed 3D navigator software for cardiac CT to support the system. SMC provided anonymized 3D CTA image data. The 3D CTA dataset, which had excellent image quality, was acquired using a dual-source 64-slice CT scanner (Definition, Siemens Medical Solutions, Germany) with a gantry rotation time of 330 ms and standard collimation of 0.6 mm. The CTA dataset comprised 412 slices that measured 512 \(\times\) 512 pixels, with a voxel size of 0.38 m \(\times\) 0.38 m \(\times\) 0.3 m. In addition, the slices were for a patient who had no plaque or luminal stenosis in coronary arteries. As our navigator software must be able to identify the 3D structure of complex organs with ease, a 3D cardiac CT navigator prototype was developed as a case study to illustrate the concept. We have created a 3D display that helps viewers examine 3D structures in greater detail thanks to enhanced depth perception. To enable 3D visualization, we followed a standard procedure to process the images from the coronary CTA images. We segmented the whole heart, coronary arteries from the original CTA dataset, and rendered the 3D volume of the original CTA images as well as segmented whole heart and coronary arteries. The rendered 3D volumes can be customized in terms of color and transparency. Moreover, 3D volume rendering is accompanied by multi-planar reconstruction (MPR) as shown in Figure 7. Furthermore, our method can be used to convert the volume to a 3D mesh. One possible usage scenario is presented in Figure 8: the 3D display serves as a 3D navigator that allows potential coronary lesions to be identified easily within complex coronary artery structures. These lesion candidates can then be examined in detail using 2D modes with 2D cardiac CT images. Figure 6: Eye center positions are refined with our proposed re-weight algorithm for eye occluded faces. From the MobileNet v2 based PFLD method (the upper row), the re-weight subnet infers the pixel confidence on the feature map from both the structure between landmarks and landmark appearance as shown in the bottom row. Figure 7: Three-dimensional cardiac CT navigator software prototype. The images show the volume rendered original CT images without any pre-processing (**1 st row**), a segmented whole heart (**2 nd row**), and segmented coronary arteries (**3 rd row**). An expert reader from SMC was asked to identify various heart structures using our autostereoscopic 3D display prototype. From a segmented 3D volume of a heart, the expert reader could identify the heart's main organs, including its major coronary artery structures, namely the left anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA).