微创癌症手术中伽马探针信号术中可视化的腹腔镜场景分析
Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery
AI总结：
针对微创癌症手术中SENSEI伽马探针信号难以在腹腔镜图像上定位的问题，本文开发了工具跟踪、位姿估计、分割、深度估计与三维重建方法，实现探针感测区域的术中可视化。
摘要：
癌症仍然是全球面临的重大健康挑战，在英国每两分钟就有一例新诊断。手术是癌症的主要治疗选择之一。然而，由于缺乏可靠的术中可视化工具，外科医生依赖触觉和肉眼，仅有限地使用术前图像数据来直接指导癌组织和转移灶的切除。这导致在切缘阳性或意外损伤其他关键结构的情况下，成本增加并对患者造成伤害。因此，迫切需要更可靠、更准确的微创手术术中可视化工具，以改善手术结果并提升患者护理。最近，一种小型化癌症检测探针（即由Lightpoint Medical Ltd.开发的SENSEI）利用核药物的癌症靶向能力，通过发射的伽马信号在术中更准确地识别癌症。然而，该探针的使用带来了可视化挑战，因为该探针是非成像的，并且与组织之间存在空气间隙，使外科医生难以在组织表面定位探针的感测区域。从几何学上讲，感测区域被定义为伽马探针轴与组织表面在三维空间中的交点，但投影到二维腹腔镜图像上。因此，在本论文中，首先开发了工具跟踪、位姿估计和分割工具，随后开发了腹腔镜图像深度估计算法和三维重建方法。
英文摘要：
Cancer remains a significant health challenge worldwide, with a new diagnosis occurring every two minutes in the UK. Surgery is one of the main treatment options for cancer. However, surgeons rely on the sense of touch and naked eye with limited use of pre-operative image data to directly guide the excision of cancerous tissues and metastases due to the lack of reliable intraoperative visualisation tools. This leads to increased costs and harm to the patient where the cancer is removed with positive margins, or where other critical structures are unintentionally impacted. There is therefore a pressing need for more reliable and accurate intraoperative visualisation tools for minimally invasive surgery to improve surgical outcomes and enhance patient care. A recent miniaturised cancer detection probe (i.e., SENSEI developed by Lightpoint Medical Ltd.) leverages the cancer-targeting ability of nuclear agents to more accurately identify cancer intra-operatively using the emitted gamma signal. However, the use of this probe presents a visualisation challenge as the probe is non-imaging and is air-gapped from the tissue, making it challenging for the surgeon to locate the probe-sensing area on the tissue surface. Geometrically, the sensing area is defined as the intersection point between the gamma probe axis and the tissue surface in 3D space but projected onto the 2D laparoscopic image. Hence, in this thesis, tool tracking, pose estimation, and segmentation tools were developed first, followed by laparoscope image depth estimation algorithms and 3D reconstruction methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
