Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks

-- T o 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. T oward 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. Kidney cancer is one of the most common forms of cancer in the US, with over 65,000 new patients being diagnosed every year, leading to over 15,000 deaths [1]. The standard treatment for localized small renal masses has shifted from radical nephrectomy (complete kidney removal) toward the more minimally invasive approach of partial nephrectomy (removal of the tumor, retaining partial kidney function). One of the main challenges during tumor removal is ensuring the resection of adequate tumor margins. This work has been submitted to the IEEE for possible publication.