Computer-Assisted Liver Surgery: from preoperative 3D patient modelling to peroperative guidance

La chirurgie représente le meilleur taux de survie pour les cancers hépatiques. Le traitement d’images médicales peut apporter une importante amélioration dans la prise en charge en guidant le geste chirurgical. Nous présentons ici une nouvelle procédure chirurgicale assistée par ordinateur incluant la modélisation 3D préopératoire du patient, suivie par une planification chirurgicale virtuelle et finalisée par un guidage peropératoire réalisé par réalité augmentée (RA). Les premières évaluations incluant des applications cliniques valident le bénéfice attendu. La prochaine étape consistera à automatiser le système de réalité augmentée peropératoire par le développement d’une salle d’opération hybride.Surgery has the best survival rate in hepatic cancer. However, such interventions cannot be undertaken for all patients as the eligibility rules for liver surgery lack accuracy and may include many exceptions. Medical image processing can lead to a major improvement of patient care by guiding the surgical gesture. We present here a new computer-assisted surgical procedure including preoperative 3D patient modelling, followed by virtual surgical planning and finalized by intraoperative computer guidance through the use of augmented reality. First evaluations including the clinical application validate the awaited benefit. The next step will consist in automating the intraoperative augmented reality system thanks to the development of a Hybrid surgical OP-room

Serial magnetic resonance imaging based assessment of the early effects of an ACE inhibitor on postinfarction left ventricular remodeling in rats

In vivo assessment of treatment efficacy on postinfarct left ventricular (LV) remodeling is crucial for experimental studies. We examined the technical feasibility of serial magnetic resonance imaging (MRI) for monitoring early postinfarct remodeling in rats. MRI studies were performed with a 7-Tesla unit, 1, 3, 8, 15, and 30 days after myocardial infarction (MI) or sham operation, to measure LV mass, volume, and the ejection fraction (EF). Three groups of animals were analyzed: sham-operated rats (n = 6), MI rats receiving lisinopril (n = 11), and MI rats receiving placebo (n = 8). LV dilation occurred on day 3 in both MI groups. LV end-systolic and end-diastolic volumes were significantly lower in lisinopril-treated rats than in placebo-treated rats at days 15 and 30. EF was lower in both MI groups than in the sham group at all time points, and did not differ between the MI groups during follow-up. Less LV hypertrophy was observed in rats receiving lisinopril than in rats receiving placebo at days 15 and 30. We found acceptable within- and between-observer agreement and an excellent correlation between MRI and ex vivo LV mass (r = 0.96; p < 0.001). We demonstrated the ability of MRI to detect the early beneficial impact of angiotensin-converting enzyme (ACE) inhibitors on LV remodeling. Accurate and noninvasive, MRI is the tool of choice to document response to treatment targeting postinfarction LV remodeling in rats

Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition

Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of current vision-based activity recognition methods relies heavily on a high volume of manually annotated data. This data is difficult and time-consuming to generate and requires domain-specific knowledge. In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos. We introduce a step-phase dependency loss to exploit the weak supervision signal. We then employ a Single-Stage Temporal Convolutional Network (SS-TCN) with a ResNet-50 backbone, trained in an end-to-end fashion from weakly annotated videos, for temporal activity segmentation and recognition. We extensively evaluate and show the effectiveness of the proposed method on a large video dataset consisting of 40 laparoscopic gastric bypass procedures and the public benchmark CATARACTS containing 50 cataract surgeries