用占用网络追踪变形肾脏中的肿瘤，实现实时3D定位
Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks
- 基于占用网络，从部分点云中重建肿瘤位置
- 在中度变形下定位误差6-10mm，速度超60Hz
- 适合机器人辅助肾部分切除术的实时导航
为实现术中肿瘤精准定位，本研究利用术前CT信息指导定位。然而手术过程中肿瘤会发生形变，导致切除不准确、手术时间延长和切缘过宽。该问题在机器人辅助部分肾切除术（RAPN）中尤为突出，因肾脏在操作中会显著变形。为此，本文提出一种基于占用网络的方法，在交互速度下实现对变形肾脏中肿瘤的定位。通过构建3D水凝胶肾脏模型，嵌入外生型与内生型肾肿瘤，模拟真实组织力学特性，实现清晰的肿瘤边界分割。实验表明，该方法可在中度变形情况下实现6-10mm定位精度，并以超过60Hz的速率提供完整的三维体积信息，可直接支持后续机器人切除任务。
原文摘要 · Abstract (English)
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.
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