#Context: Liver cancer is a leading cause of cancer death worldwide. An estimated 830,000 people around the world died from the disease in 2020. Liver resection is considered as one of the most effective treatments. In this respect, laparoscopic liver resection (LLR) comes up by reducing substantially patient trauma compared to open liver resection. The patient recovers faster which in return reduces healthcare costs. However the use of LLR remains limited. This is because of three challenges. First, controlling intraoperative bleeding using laparoscopic instruments requires advanced technical skills.

Second, the surgeon cannot manually palpate the liver and thus cannot locate the tumours and their resection margins easily.
Consequently this raises a risk of inadequate resection on the patient’s liver such as the removal of too much healthy tissue and the leaving of some cancerogenous tissue behind. Third, laparoscopic ultrasonography (LUS), the only tool for intraoperative subsurface imaging which allows real-time tumour localisation, has a long learning curve.
This is because its design consists of a small transducer with a small field of view attached to the end of a long shaft with a pivoting mechanism. In order to ease LLR, augmented reality (AR) based methods relying on preoperative data were proposed [1,2]. These AR-based methods predict the location of the tumours by overlaying the preoperative data onto the laparoscopy image. These methods require the whole liver to be visible as much as possible in the laparoscopy image to make a reliable prediction. However, the liver is usually very partially visible (i.e., about 30% or less).
Although these methods are useful to guide surgeons at the very beginning of surgery, they are neither real-time nor automatic.
[1] “Combining Visual Cues with Interactions for 3D-2D Registration in Liver Laparoscopy”, Annals of Biomedical Engineering, 2020.
[2] “Augmented Reality Guidance in Laparoscopic Hepatectomy with Deformable Semi-automatic Computed Tomography Alignment”, Journal of Visceral Surgery, 2019.

#Links:
http://igt.ip.uca.fr/~ab/
https://erol-papers.github.io

#Research:We are looking for one highly motivated postdoctoral fellow to study on multimodal liver tumour registrations and augmentations to be able to guide the surgeons during LLR. The postdoctoral fellow will focus on two open problems.

1/ Automatic and real-time deformable registration of a preoperative CT volume to an intraoperative LUS image without any additional tracker sensor.
2/ Augmentation of the subsurface liver tumours and veins in the laparoscopy images (i.e., occluded object visualisation) on a flat screen with the relevant depth cues such that their depth can be conveyed to the surgeon accurately.

The successful outcome of the position will simplify mini-invasive liver surgery. It will shorten hospital stays, improve surgical safety and accuracy, and contribute to an overall better quality of patient life and reduction of healthcare costs.

#Requirements: Applicant must have:
1/ PhD degree in Computer Science;
2/ Excellent programming skills in C++ and python;
3/ Strong background in computer vision and experience in augmented reality;

4/ Proficiency in written and spoken English language.

#Application:Applicant must submit: (1) a one-page cover letter;(2) curriculum vitae with publications list; (3) Two reference letters;(4) The earliest possible starting date. All should be sent, in a SINGLE PDF document, with the email subject[Postdoc application – CVAR for LLR] to:

erol.ozgur@sigma-clermont.fr and mohammad.alkhatib@sigma-clermont.fr

Once we receive your application and if it fits well for the position, then you will be contacted within two weeks.