A Survey of Needle Steering Approaches in Minimally Invasive Surgery

In virtue of a curved insertion path inside tissues, needle steering techniques have revealed the potential with the assistance of medical robots and images. The superiority of this technique has been preliminarily verified with several maneuvers: target realignment, obstacle circumvention, and multi-target access. However, the momentum of needle steering approaches in the past decade leads to an open question-"How to choose an applicable needle steering approach for a specific clinical application?" This survey discusses this question in terms of design choices and clinical considerations, respectively. In view of design choices, this survey proposes a hierarchical taxonomy of current needle steering approaches. Needle steering approaches of different manipulations and designs are classified to systematically review the design choices and their influences on clinical treatments. In view of clinical consideration, this survey discusses the steerability and acceptability of the current needle steering approaches. On this basis, the pros and cons of the current needle steering approaches are weighed and their suitable applications are summarized. At last, this survey concluded with an outlook of the needle steering techniques, including the potential clinical applications and future developments in mechanical design.

Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention

PURPOSE

Understanding the three-dimensional anatomy of percutaneous intervention in prostate cancer is essential to avoid complications. Recently, attempts have been made to use machine learning to automate the segmentation of functional structures such as the prostate gland, rectum, and bladder. However, a paucity of material is available to segment extracapsular structures that are known to cause needle deflection during percutaneous interventions. This research aims to explore the feasibility of the automatic segmentation of prostate and extracapsular structures to predict needle deflection.

METHODS

Using pelvic magnetic resonance imagings (MRIs), 3D U-Net was trained and optimized for the prostate and extracapsular structures (bladder, rectum, pubic bone, pelvic diaphragm muscle, bulbospongiosus muscle, bull of the penis, ischiocavernosus muscle, crus of the penis, transverse perineal muscle, obturator internus muscle, and seminal vesicle). The segmentation accuracy was validated by putting intra-procedural MRIs into the 3D U-Net to segment the prostate and extracapsular structures in the image. Then, the segmented structures were used to predict deflected needle path in in-bore MRI-guided biopsy using a model-based approach.

RESULTS

The 3D U-Net yielded Dice scores to parenchymal organs (0.61-0.83), such as prostate, bladder, rectum, bulb of the penis, crus of the penis, but lower in muscle structures (0.03-0.31), except and obturator internus muscle (0.71). The 3D U-Net showed higher Dice scores for functional structures ([Formula: see text]0.001) and complication-related structures ([Formula: see text]0.001). The segmentation of extracapsular anatomies helped to predict the deflected needle path in MRI-guided prostate interventions of the prostate with the accuracy of 0.9 to 4.9 mm.

CONCLUSION

Our segmentation method using 3D U-Net provided an accurate anatomical understanding of the prostate and extracapsular structures. In addition, our method was suitable for segmenting functional and complication-related structures. Finally, 3D images of the prostate and extracapsular structures could simulate the needle pathway to predict needle deflections.

Intraoperative optimization of seed implantation plan in breast brachytherapy

PURPOSE

Low-dose-rate permanent-seed (LDR-PS) brachytherapy has shown a great potential for treating breast cancer. An implantation scheme indicating the template pose and needle trajectories is determined before the operation. However, when performing the pre-planned scheme intraoperatively, a change of the patient's posture will cause seed placements away from the desired locations. Hence, the implantation scheme should update based on the current patient's posture.

METHODS

A numerical method of optimizing the implantation scheme for the LDR-PS breast brachytherapy is presented here. The proposed algorithm determines the fewest needle trajectories and template poses for delivering the seeds to the intraoperative desired locations. The clinical demand, such as the minimum distance between the chest wall and the needle, is considered in the optimization process.

RESULTS

The method was simulated for a given LDR-PS brachytherapy procedure to evaluate the optimal scheme as the number of the template poses changing. The optimization parameters of the needles' number and the implantation errors are used to adjust the algorithm outcome. The results show that the implantation schemes obtained by our method have a satisfactory accuracy in the cases of 2 or 3 template poses. The computation time is about 76s to 150s according to the number of the template poses from 1 to 3.

CONCLUSION

The proposed method can find the optimal implantation scheme corresponding to the current desired seed locations immediately once there is a change of patient's posture. This work can be applied to the robot-assisted LDR-PS breast brachytherapy for improving the operation accuracy and efficiency.