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.

翻译：为在手术中追踪肿瘤，需利用术前CT扫描信息确定其位置。然而，随着外科医生进行手术操作，肿瘤可能发生形变，这为精准切除肿瘤带来重大障碍，可能导致手术误差增大、手术时间延长及切除边界过度等问题。该问题在机器人辅助肾部分切除术（RAPN）中尤为突出，因为肾脏在手术过程中会发生显著形变。为解决此问题，我们提出一种基于占据网络的方法，可在交互速率下对形变肾脏模型内的肿瘤进行定位。我们通过构建嵌入外生型和内生型肾肿瘤的3D水凝胶肾脏模型来验证该方法。该模型高度模拟真实组织力学特性以再现活体手术中的肾脏形变，同时提供优异的对比度和清晰的肿瘤边界，支持基于自动阈值的分割。研究结果表明，所提方法可在中度形变的肾脏中以6-10毫米的边界误差定位肿瘤，并以超过60Hz的频率提供关键的三维体积信息。该能力可直接支持机器人切除等下游任务。