# Autostereoscopic 3D Display System for 3D Medical Images
## 3 Results and Discussion
### Experimental Results on Autostereoscopy Visualization Systems for 3D Cardiac CT  
For running the cardiac software, we used a Windows 10 personal computer equipped with a 2.90 GHz Intel Core i7-10700 CPU and a NVIDIA GeForce RTX 2060 GPU. Three-dimensional light-field rendering of the cardiac CT 3D volume was performed using the light field 3D subpixel rendering method. All processes ran in real time without suffering delays. The proposed autostereoscopic 3D display system's crosstalk averaged 8.32%, comparable to those of 3D displays requiring the viewer to wear glasses. By adjusting the disparity between the stereo images, a depth of 20 cm was achieved using the dynamic light-field rendering method (Figure 12). If the left and right images are not separated, they exhibit 3D crosstalk, which results in users seeing double images (Figure 13). We have received no complaints regarding any discomfort or 3D fatigue from the expert reader after evaluating our 3D autostereoscopy. Using our 3D autostereoscopy system, the reader comfortably identified the 3D heart structure, including the four chambers, aorta, and main coronary arteries (LAD, LCX, and RCA). Assisted by the proposed 3D autostereoscopic display, the reader could discern more geometric information, such as overlapping coronary structures, relative to conventional 2D displays. This is because the proposed 3D display contains two different image views from different angles, allowing the user to see two distinct sets of information simultaneously (Figure 13). Figure 14 shows an enlarged region of interest (red box, 1 st row), which clearly shows the morphology of different coronary arteries: the arteries are separated in the left image, while the right image shows overlapping information. Figure 11: Illustration of the proposed eye tracking method with pupil segmentation modules. The green circles around the red points on the eyes indicate the pupil segmentation modules, which increase the accuracy of the eye tracking. The left side shows the left camera image from a stereo webcam, while the right side shows the image captured by the right camera. The left and right images were combined to calculate the 3D eye position via stereo image matching based on triangular interpolation. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Performance on Clean Faces**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Performance on Occluded** \\ **Faces** \\ \end{tabular} } \\ \hline Illumination condition & \multicolumn{2}{c}{300–400 lux} \\ Distance to users from camera & \multicolumn{2}{c}{\(1\) m} \\ Detection accuracy & 99.80\% & 99.80\% \\ Tracking precision (mm) & 1.5 mm & 6.5 mm \\ Speed (fps) & 250 fps & 100 fps \\ Testing DB number & 20,000 & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of the proposed eye tracking algorithm. Although the performance evaluation by the expert reader was limited to a single clinical example, they reported that the 3D structure identification process was faster and easier using the proposed system. Nevertheless, it is clear that our 3D autostereoscopic display system must undergo further investigation and quantitative evaluation before being widely adopted in clinical settings. Significantly, our 3D display can function with any other medical image modality that is compatible with images captured as multiple 3D slices. Examples of such modalities are shown in Figure 15. In addition, surgical training and planning can be improved with our eye tracking based autostereoscopic 3D display method, by providing accurate depth perception without special 3D glasses. From a clinical standpoint, it is essential for virtual reality and 3D display systems to have adequate mobility, vision, usability, immersion, and accurate depth of field [46; 47]. Our proposed method fulfills the mobility, vision, and usability in the surgical field by providing a wide viewing range through the proposed eye tracking algorithm.