Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks

TL;DR

This paper presents an occupancy network-based method for real-time tumor localization in deformed kidneys during surgery, improving accuracy and speed for robotic tumor resection.

Contribution

The study introduces a novel occupancy network approach for tracking tumors in deforming organs using partial point clouds, validated on realistic kidney phantoms.

Findings

Localizes tumors within 6-10mm margin during deformation

Operates at over 60Hz for real-time application

Provides detailed 3D volumetric information

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.