Laparoscopic scene analysis for intraoperative visualisation of gamma probe signals in minimally invasive cancer surgery
File(s)
Author(s)
Huang, Baoru
Type
Thesis
Abstract
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.
The problem of detecting the probe axis-tissue intersection point was then transformed to laser point position inference using a custom laser module. Both the hardware and software design of the proposed solution were illustrated. The best detection results were achieved using a simple network design, allowing real-time inference of the sensing area, establishing a new benchmark for the surgical vision community.
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.
The problem of detecting the probe axis-tissue intersection point was then transformed to laser point position inference using a custom laser module. Both the hardware and software design of the proposed solution were illustrated. The best detection results were achieved using a simple network design, allowing real-time inference of the sensing area, establishing a new benchmark for the surgical vision community.
Version
Open Access
Date Issued
2024-03
Date Awarded
2024-08
Copyright Statement
Creative Commons Attribution NonCommercial NoDerivatives Licence
Advisor
Elson, Daniel
Giannarou, Stamatia
Sponsor
National Institute for Health Research (Great Britain)
Grant Number
NIHR200035
Publisher Department
Department of Surgery & Cancer
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)