Ultrasound Based Planning and Navigation for Non-Anatomical Liver Resections – An Ex-Vivo Study

Hybrid optical-vision tracking in laparoscopy: accuracy of navigation and ultrasound reconstruction

INTRODUCTION

The use of laparoscopic and robotic liver surgery is increasing. However, it presents challenges such as limited field of view and organ deformations. Surgeons rely on laparoscopic ultrasound (LUS) for guidance, but mentally correlating ultrasound images with pre-operative volumes can be difficult. In this direction, surgical navigation systems are being developed to assist with intra-operative understanding. One approach is performing intra-operative ultrasound 3D reconstructions. The accuracy of these reconstructions depends on tracking the LUS probe.

MATERIAL AND METHODS

This study evaluates the accuracy of LUS probe tracking and ultrasound 3D reconstruction using a hybrid tracking approach. The LUS probe is tracked from laparoscope images, while an optical tracker tracks the laparoscope. The accuracy of hybrid tracking is compared to full optical tracking using a dual-modality tool. Ultrasound 3D reconstruction accuracy is assessed on an abdominal phantom with CT transformed into the optical tracker's coordinate system.

RESULTS

Hybrid tracking achieves a tracking error < 2 mm within 10 cm between the laparoscope and the LUS probe. The ultrasound reconstruction accuracy is approximately 2 mm.

CONCLUSION

Hybrid tracking shows promising results that can meet the required navigation accuracy for laparoscopic liver surgery.

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach

Purpose

Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries.

Approach

In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS. The model was trained on B-mode intraoperative hepatic US images, hand-labeled by an expert clinician. The model was tested on an independent set of similar images. The average age of the study population was 61.9 ± 9.9 years. Ground truth for the test set was provided by a radiologist, and three extra delineation sets were used for the computation of inter-observer variability.

Results

The presented model achieved a DSC of 0.84 (p = 0.0037 ), which is comparable to the expert human raters scores. The model segmented hypoechoic and mixed lesions more accurately (DSC of 0.89 and 0.88, respectively) than hyper- and isoechoic ones (DSC of 0.70 and 0.60, respectively) only missing isoechoic or >20 mm in diameter (8% of the tumors) lesions. The inclusion of extra margins of probable tumor tissue around the lesions in the training ground truth resulted in lower DSCs of 0.75 (p = 0.0022 ).

Conclusion

The model can accurately segment hepatic tumors from iUS images and has the potential to speed up the resection margin definition during surgeries and the detection of lesion in screenings by automating iUS assessment.