Precise craniotomy localization is essential in neurosurgical procedures, especially during the preoperative planning. The mainstream craniotomy localization method utilizing image-guided neurosurgery system (IGNS) or augmented reality (AR) navigation system require experienced neurosurgeons to point out the lesion margin by probe and draw the craniotomy manually on the patient's head according to cranial anatomy. However, improper manual operation and dither from the AR model will bring in errors about craniotomy localization. In addition, there is no specific standard to evaluate the accuracy of craniotomy. This paper attempts to propose a standardized interactive 3D method using orthogonal transformation to map the lesion onto the scalp model and generate a conformal virtual incision in real time. Considering clinical requirements, the incision can be amended by 3D interaction and margin modification. According to the IGNS and the virtual incision, an actual craniotomy will be located on the patient's head and the movement path of the probe will be recorded and evaluated by an indicator, which is presented as an evaluated standard to measure the error between virtual and actual craniotomies. After the experiment, an incision is drawn on a 3D printing phantom based on the generated virtual one. The results show that the proposed method can generate a lesion-consistent craniotomy according to the size of the lesion and the mapping angle and delineate the incision on the patient's head precisely under the IGNS.

The Internet of Things (IoT) in the operating room can aid to improve the quality of the computer-aided surgical system. Patient-to-image registration is an important issue for computer-aided surgical systems. Automating the procedure of patient-to-image registration could increase tracking accuracy and lower the time consumed for performing the procedure of registration. Therefore, we propose an automatic registration method to address this issue by constructing a wireless sensor network system for surgery. A plastic fiducial object combing with specific markers is developed to perform registration in that the ultimate purpose is to integrate them into a surgical robotic system for surgical navigation. The specific markers are designed to localize the position of the small EM sensor and can be automatically detected in CT/MRI images by an automatic algorithm. The positions of the EM tracking sensors can be calibrated during the procedure of registration. Some experiments are designed and performed, and the experimental results demonstrate that the proposed registration method is robust and accurate. The proposed registration method is a foundational link of the surgical robots combing with virtual or augmented reality technology that all these technologies will be performed in further surgical navigation.