Abdominal Cancer
Conditions
Keywords
Surgical Navigation, Ultrasound, Registration, Electromagnetic Tracking, Pelvic Malignancies, Deep Learning, Segmentation
Brief summary
In this study we aim to develop an automatic artery and bone segmentation algorithm, which is required for future clinical implementation of US registration for surgical navigation. Various registration methods will be evaluated with the data of this study to obtain most optimal results. If automatic segmentation and registration is successful, the final accuracy of the developed US registration method for tumor tracking should be evaluated in future studies in patients eligible for surgical navigation. Eventually, we aim to replace the CBCT-scan with an automatic tracked US registration pipeline for a more efficient and accurate registration procedure, which could improve the applicability and accuracy of surgical navigation and patient outcomes.
Detailed description
Image-guided navigation surgery allows for full utilization of pre-operative imaging during surgery and has the potential of reducing both irradical resections and morbidity. To use navigation, a registration procedure is required to correlate pre-operative imaging with the patient's position on the operating room (OR). Currently, registration is done by Cone-Beam CT (CBCT) scanning on the OR prior to navigation surgery. However, the main limitation of the CBCT method is that it cannot compensate for per-operative changes such as bed rotation, retractor placement and tissue displacement due to the surgery. Alternatively, by using intra-operative tracked ultrasound and vessel-based patient registration, changing conditions during surgery can better be dealt with. This improved patient registration method could lead to an increased navigation accuracy and improved clinical usability and outcomes. The main difference between CBCT and proposed ultrasound registration is that CBCT is based on bones, while the ultrasound is based on vessels. Bones can be very easily imaged on the CBCT and therefore used for bone-bone registration with pre-operative CT-scans. However, vessels are more difficult to acquire, especially with ultrasound, and an automatic registration process with pre-operative imaging is needed for efficient clinical usability. For this, the vessels need to be extracted from the tracked ultrasound images to create a 3D representation that can be registered. Therefore, an algorithm needs to be developed that can automatically segment the pelvic vessels from ultrasound images.
Interventions
PROCEDUREIntra-operative ultrasound measurement
A patient-specific 3D model will be created using an available pre-operative CT scan. Anatomical target points are selected on this virtual model before the start of the surgery. Prior to surgery, a patient-reference electromagnetic (EM) sensor will be placed between the patient and the matrass on the operating table to account for patient movement during acquisition. Intra-operatively, an initial point registration of the 3D model with the electromagnetic tracking system (EMTS) will be performed based on ultrasound (US) imaging of the arterial bifurcations by the surgeon. Then, the surgeon will acquire multiple US sweeps of the pelvic bone (pubic bone, sacrum and iliac crests) and arteries (abdominal aorta and left and right iliac arteries). For validation purposes, the pre-operatively defined anatomical target points will be visualized on US imaging and by pinpointing the EM tracked pointer. All tracking and US data will be recorded and stored for post-operative analysis.
Sponsors
The Netherlands Cancer Institute
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Intervention model description
One group to evaluate the feasibility and accuracy of the proposed methods.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* ≥ 18 years old * Scheduled for laparotomy (first 30 patients) or robotic assisted lymph node dissection (second 20 patients) * A clinical pre-operative CT scan is available * Patient provides written informed consent
Exclusion criteria
* Metal implants which could influence the 3D modelling or tracking accuracy * Patients with a pacemaker or defibrillator * Patient received treatment, e.g. surgery or radiotherapy, between the pre-operative CT scan and surgery, which might altered the patient's anatomy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of developed automatic segmentation algorithm | One day | An automatic segmentation algorithm for real-time intra-operative vessel and bone segmentation from tracked US images will be developed. The accuracy of this network will be evaluated using the Dice similarity coefficient, ranging from 0 to 1 where a higher score means a better outcome. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of intra-operative ultrasound registration | One day | To evaluate the accuracy of different patient registration methods of intra-operative US imaging with pre-operative CT imaging, such as 3D model or centerline registration. |
| Usability of intra-operative ultrasound registration | One day | Evaluation of the intraoperative usability of the tracked US device and visualization of vessels and bone by the surgeons will be evaluated using the system usability scale (SUS). This scale ranges from 0-100 where a higher score means a better outcome. |
Countries
Netherlands
Outcome results
None listed