标题： 腹腔镜场景分析用于微创癌症手术中伽马探测器信号的术中可视化 显示英文标题

标题： Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery

摘要： 癌症仍然是全球重大的健康挑战，英国每两分钟就有一例新的癌症诊断。 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，外科医生只能依靠触觉和肉眼，有限地使用术前影像数据来直接指导癌变组织和转移灶的切除。 这导致了在癌症切除边缘为阳性的病例中，或者在无意中影响其他关键结构的情况下，增加了成本并给患者带来伤害。 因此，迫切需要更可靠和准确的术中可视化工具，用于微创手术，以改善手术结果并提高患者护理水平。 最近开发的一种微型癌症检测探针（即Lightpoint Medical Ltd.公司开发的SENSEI）利用核制剂的癌症靶向能力，通过发射的伽马信号更准确地在术中识别癌症。 然而，该探针的使用存在可视化挑战，因为该探针是非成像的，并且与组织之间存在空气间隙，使得外科医生难以在组织表面定位探针的感应区域。 从几何学上讲，感应区域被定义为伽马探针轴线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、姿态估计和分割工具，随后开发了腹腔镜图像深度估计算法和三维重建方法。 显示英文摘要

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.