#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 tumors 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 cancerogenic tissue behind. Third, laparoscopic ultrasonography (LUS), the only tool for intraoperative subsurface imaging which allows real-time tumor localization, 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, robotic solutions would provide great assistance by controlling the LUS. For that, the goal of this PhD is to develop and implement a solution for contact servoing of a flexible ultrasound probe on the deformable liver surface. The setup includes a robotic arm mounted with a flexible ultrasound probe, a camera and a phantom liver organ. Contact servoing will be based on the images of the camera and the ultrasound probe.

#Research:

We are looking for one highly motivated PhD student to study multimodal contact servoing on a deformable liver

The PhD student will focus on three open problems:

1. Automatic segmentation of the liver;

2. Multimodal contact servoing:

a. bring the ultrasound probe in contact with the liver;

b. to move the probe on the liver surface.

The PhD will be in close collaboration with the scientists and surgeons and the successful outcome of this PhD 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:

1/ undergraduate and graduate degrees in Computer Science, Robotics or closely related fields;

2/ excellent programming skills in C++ and python;

3/ strong theoretical and applied background in computer vision, machine learning, visual servoing and robotics;

4/ proficiency in written and spoken English language.

#Application:

Applicants must submit

1/ a one-page cover letter,

2/ curriculum vitae with publications list and contacts of 2 references,

3/ a copy of academic transcripts (bachelor/master grades),

4/ availability (the earliest feasible starting date).

Applicants must be prepared to provide two reference letters upon request.

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

Applications should be sent, *in a single PDF document*, with the email subject [PhD application] to:

youcef.mezouar@sigma-clermont.fr; erol.ozgur@sigma-clermont.fr; mohammad.alkhatib@sigma-clermont.fr