Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks

Henrich P, Liu J, Ge J, Schmidgall S, Shepard L, Ghazi AE, Mathis-Ullrich F, Krieger A (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

IEEE/RSJ International Conference on Intelligent Robots and Systems IEEE

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 7227-7234

Conference Proceedings Title: IEEE International Conference on Intelligent Robots and Systems

Event location: Abu Dhabi AE

ISBN: 9798350377705

DOI: 10.1109/IROS58592.2024.10802114

Abstract

To track tumors during surgery, information from preoperative CT scans is used to determine their position. However, as the surgeon operates, the tumor may be deformed which presents a major hurdle for accurately resecting the tumor, and can lead to surgical inaccuracy, increased operation time, and excessive margins. This issue is particularly pronounced in robot-assisted partial nephrectomy (RAPN), where the kidney undergoes significant deformations during operation. Toward addressing this, we introduce a occupancy network-based method for the localization of tumors within kidney phantoms undergoing deformations at interactive speeds. We validate our method by introducing a 3D hydrogel kidney phantom embedded with exophytic and endophytic renal tumors. It closely mimics real tissue mechanics to simulate kidney deformation during in vivo surgery, providing excellent contrast and clear delineation of tumor margins to enable automatic threshold-based segmentation. Our findings indicate that the proposed method can localize tumors in moderately deforming kidneys with a margin of 6mm to 10mm, while providing essential volumetric 3D information at over 60Hz. This capability directly enables downstream tasks such as robotic resection.

Authors with CRIS profile

Pit Henrich Professur für Robotische Planung und Kognition in der Chirurgie Franziska Mathis-Ullrich Professur für Robotische Planung und Kognition in der Chirurgie

Involved external institutions

Johns Hopkins University (JHU) US United States (USA) (US)

How to cite

APA:

Henrich, P., Liu, J., Ge, J., Schmidgall, S., Shepard, L., Ghazi, A.E.,... Krieger, A. (2024). Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks. In IEEE International Conference on Intelligent Robots and Systems (pp. 7227-7234). Abu Dhabi, AE: Institute of Electrical and Electronics Engineers Inc..

MLA:

Henrich, Pit, et al. "Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks." Proceedings of the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024, Abu Dhabi Institute of Electrical and Electronics Engineers Inc., 2024. 7227-7234.

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