Abstract
In minimally invasive surgery, where a direct sight of the treated organ or structures is not feasible but with a laparoscope, image guided systems have been developed to provide the surgeon with real time information of the surgical area, in the form of images of the anatomy of the patient, and surgical navigation. In order to do so, image-to-patient and image-to-image registration techniques are required to bring physical and image information on to the same system of reference. Essentially, both methods rely on point-to-point correspondence, using a collection of selected features on both the fixed and target domains. In the case of image-to-patient, position landmarks are used. Whereas in image-to-image, features related to the intensity information of the image, or the topology of segmentations, are the usual approach. Nevertheless, these procedures require time and are susceptible to human error. In image-to-patient in particular, when sampling landmarks on the patient anatomy. Image-to-image methods on the other hand, operate between different images and virtual models, being complex to implement and to achieve good results, especially in multi-modal setups.
The presented research is framed in the field of hepatocellular carcinoma surgical treatment and colorectal liver metastasis, the most common types of liver
cancer. Wedge resection is the usual choice to remove the cancerous cells, aiming to spare as much healthy tissue as possible while ensuring a complete extraction of the tumour. However, during the intervention, the organ undergoes major deformation due to mobilisation and detachment of the abdominal wall. This deformation is not reflected in the pre-operative images, showing an inaccurate location of relevant anatomical structures as well as the target lesion. Through a laparoscope and laparoscopic ultrasound, the surgeon can build a mental image of the anatomy of the patient during the intervention, adding extra strain on the practitioner. Registration techniques can leverage the procedure outcome and ease the surgery by aligning the virtual models of the patient to the situation on the surgical table. Furthermore, by updating the pre-operative model with the real-time information acquired when inspecting the organ.
In particular, the present study focuses on the use of registration for navigation in minimally invasive interventions, including image-to-patient and image-to-image registration. Main contributions include the evaluation of tracking technologies for surgical navigation; a novel image-to-patient registration method (single landmark registration method) which can take advantage of the laparoscopic ultrasound to improve the registration of the pre-operative images; the use of deep learning for image-to-image registration in medical applications, and development of new training methods; and the research on the influence of human accuracy in landmark sampling during image-to-patient registration, in augmented reality applications for surgical navigation.
The presented research is framed in the field of hepatocellular carcinoma surgical treatment and colorectal liver metastasis, the most common types of liver
cancer. Wedge resection is the usual choice to remove the cancerous cells, aiming to spare as much healthy tissue as possible while ensuring a complete extraction of the tumour. However, during the intervention, the organ undergoes major deformation due to mobilisation and detachment of the abdominal wall. This deformation is not reflected in the pre-operative images, showing an inaccurate location of relevant anatomical structures as well as the target lesion. Through a laparoscope and laparoscopic ultrasound, the surgeon can build a mental image of the anatomy of the patient during the intervention, adding extra strain on the practitioner. Registration techniques can leverage the procedure outcome and ease the surgery by aligning the virtual models of the patient to the situation on the surgical table. Furthermore, by updating the pre-operative model with the real-time information acquired when inspecting the organ.
In particular, the present study focuses on the use of registration for navigation in minimally invasive interventions, including image-to-patient and image-to-image registration. Main contributions include the evaluation of tracking technologies for surgical navigation; a novel image-to-patient registration method (single landmark registration method) which can take advantage of the laparoscopic ultrasound to improve the registration of the pre-operative images; the use of deep learning for image-to-image registration in medical applications, and development of new training methods; and the research on the influence of human accuracy in landmark sampling during image-to-patient registration, in augmented reality applications for surgical navigation.