Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentortrainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons' psychomotor skills. Ongoing development of tracking systems and software Solutions has allowed for the expansión of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentáis of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as 1 To whom correspondence and reprint requests should be addressed at Bioengineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM). Avda Complutense, 30, 28040, Madrid, Spain. E-mail: ioropesa@gbt.tfo. upm.es. well as optimize their already overloaded training schedules.

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We propose a fully automated methodology for hexahedral meshing of patient-specific structures of the human knee obtained from magnetic resonance images, i.e. femoral/tibial cartilages and menisci. We select eight patients from the Osteoarthritis Initiative and validate our methodology using MATLAB on a laptop computer. We obtain the patient-specific meshes in an average of three minutes, while faithfully representing the geometries with well-shaped elements. We hope to provide a fundamentally different means to test hypotheses on the mechanisms of disease progression by integrating our patient-specific FE meshes with data from individual patients. Download both our meshes and software at http://im.engr.uconn.edu/downloads.php .