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--- courses:456:2023:projects:456-2023-07:project-07 [2023/05/15 17:06] – [Result and Discussion] zshe1
+++ courses:456:2023:projects:456-2023-07:project-07 [2023/05/15 17:31] (current) – [Real-time Integration of 2D-3D Pelvic Registration with Robotic X-ray Acquisition] zshe1
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 ======Real-time Integration of 2D-3D Pelvic Registration with Robotic X-ray Acquisition======
-**Last updated: 2023/2/9**
+**Last updated: 2023/5/15**
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 ===1. Landmark Detector===
+In our experiments on pelvic registration, we utilized sequential X-ray images and high-lighted computed landmarks on one example image (as shown in Figure below). Overall, the
+landmark detection process worked well, with most of the landmarks being located correctly.
 {{ :courses:456:2023:projects:456-2023-07:2dlandmarks.png?600 |}}
+In most cases, through manual inspection, the total number of detected landmarks is slightly less than the total number of visible landmarks. We have tried several threshold
+values until the inference result is satisfactory. Figure below shows how the number of detected landmarks varies along with the threshold values.
+{{ :courses:456:2023:projects:456-2023-07:landmarks_for_threshold.png?600 |}}
+===2. Registration Solver===
+Figure below shows the result of the registration solver of xReg. The intensity features are
+projected onto the X-ray images to help the user verify whether the registration result is
+sufficiently correct. The figure proves that our workflow performs well under the experiment
+setups.
+{{ :courses:456:2023:projects:456-2023-07:results.png?200 |}}
+To obtain correct results, we have tried different combinations of the key parameters used in the optimization process. Besides, we have found out that xReg only takes
+L./R.IOF, L./R.ASIS, and L./R.FH as its optimizing targets. Among the key parameters involved, the extrinsic parameter of the camera affects the result most significantly. Bad
+assignment of the extrinsics could lead to a poor initialization for the bound of the searching area, which eventually causes failure or completely wrong results.
+Again, as our objective is to provide an integration of xReg, the quality of the output of the package will not be discussed here. Nevertheless, with the package implemented,
+the performance of xReg and other potential methods can be compared and evaluated by a controlled dataset with known ground-truth.
+Once the registration problem has been computed, we utilize the estimated registration pose to render the pelvis with respect to the projective coordinate frame. The result of this process is shown in the figure below. The successful rendering of the pelvis with respect to the projective coordinate frame demonstrates the versatility of our approach.
+{{ :courses:456:2023:projects:456-2023-07:projection_visualization.png?600 |}}
+======Conclusion and future work======
+In conclusion, our key contributions in this project includes:
+     -  We have successfully built a new unified and modularized architecture in Python that provides developer interfaces for future modularization and development.
+     - By integrating SyntheX and xReg into our program, we have demonstrated our program's compatibility and effectiveness, and have highlighted the potential for its use in a wide range of medical imaging applications.
+     - The package can be easily installed and utilized with simple terminal commands, making it accessible to users of different levels.
+  To ensure that users can effectively leverage the package to accelerate their research, we have created detailed documentation for both SyntheX and XREGI. This documentation provides users with the necessary resources to effectively use the package.
+In the future, we will keep maintaining XREGI and make it more versatile. We seek to incorporating our implementation with robotics medical imaging platforms to realize real-time image acquisition and registration. Moreover, we plan to employ the registration result derived by XREGI in mixed reality area, such as the use of HoloLens, to facilitate its clinical applications in intraoperative processes. Additionally, we plan to submit our work to a peer-reviewed conference or journal to further validate the effectiveness and potential of our package.
 ======Dependencies======
 {{ :courses:456:2023:projects:456-2023-07:depe2.png?600 |}}

courses/456/2023/projects/456-2023-07/project-07.1684170360.txt.gz · Last modified: 2023/05/15 17:06 by zshe1