3D tracking of laparoscopic instruments using statistical and geometric modeling

International audienceDuring a laparoscopic surgery, the endoscope can be manipulated by an assistant or a robot. Several teams have worked on the tracking of surgical instruments, based on methods ranging from the development of specific devices to image processing methods. We propose to exploit the instruments' insertion points, which are fixed on the patients abdominal cavity, as a geometric constraint for the localization of the instruments. A simple geometric model of a laparoscopic instrument is described, as well as a parametrization that exploits a spherical geometric grid, which offers attracting homogeneity and isotropy properties. The general architecture of our proposed approach is based on the probabilistic Condensation algorithm

Visual servoing of a robotic endoscope holder based on surgical instrument tracking

International audienceWe propose an image-based control for a roboticendoscope holder during laparoscopic surgery. Our aim is toprovide more comfort to the practitioner during surgery byautomatically positioning the endoscope at his request. To doso, we propose to maintain one or more instruments roughly atthe center of the laparoscopic image through different commandmodes. The originality of this method relies on the direct useof the endoscopic image and the absence of artificial markersadded to the instruments. The application is validated on a testbench with a commercial robotic endoscope holder

Segmentation de scènes extérieures à partir d'ensembles d'étiquettes à granularité et sémantique variables

International audienceIn this work, we present an approach that leverages multiple datasets annotated using different classes (different labelsets) to improve the classification accuracy on each individual dataset. We focus on semantic full scene labeling of outdoor scenes. To achieve our goal, we use the KITTI dataset as it illustrates very well the focus of our paper : it has been sparsely labeled by multiple research groups over the past few years but the semantics and the granularity of the labels differ from one set to another. We propose a method to train deep convolutional networks using multiple datasets with potentially inconsistent labelsets and a selective loss function to train it with all the available labeled data while being reliant to inconsistent labelings. Experiments done on all the KITTI dataset's labeled subsets show that our approach consistently improves the classification accuracy by exploiting the correlations across data-sets both at the feature level and at the label level.Ce papier présente une approche permettant d'utiliser plusieurs bases de données annotées avec différents ensembles d'étiquettes pour améliorer la précision d'un classifieur entrainé sur une tâche de segmentation sémantique de scènes extérieures. Dans ce contexte, la base de données KITTI nous fournit un cas d'utilisation particulièrement pertinent : des sous-ensembles distincts de cette base ont été annotés par plusieurs équipes en utilisant des étiquettes différentes pour chaque sous-ensemble. Notre méthode permet d'entraîner un réseau de neurones convolutionnel (CNN) en utilisant plusieurs bases de données avec des étiquettes possiblement incohérentes. Nous présentons une fonction de perte sélective pour entrainer ce réseau et plusieurs approches de fusion permettant d'exploiter les corrélations entre les différents ensembles d'étiquettes. Le réseau utilise ainsi toutes les données disponibles pour améliorer les performances de classification sur chaque ensemble. Les expériences faites sur les différents sous-ensembles de la base de données KITTI montrent comment chaque proposition contribue à améliorer le classifieur

Mixed Pooling Neural Networks for Color Constancy

International audienceColor constancy is the ability of the human visual system to perceive constant colors for a surface despite changes in the spectrum of the illumination. In computer vision, the main approach consists in estimating the illuminant color and then to remove its impact on the color of the objects. Many image processing algorithms have been proposed to tackle this problem automatically. However, most of these approaches are handcrafted and mostly rely on strong empirical assumptions, e.g., that the average reflectance in a scene is gray. State-of-the-art approaches can perform very well on some given datasets but poorly adapt on some others. In this paper, we have investigated how neural networks-based approaches can be used to deal with the color constancy problem. We have proposed a new network architecture based on existing successful hand-crafted approaches and a large number of improvements to tackle this problem by learning a suitable deep model. We show our results on most of the standard benchmarks used in the color constancy domain

Semantic Segmentation via Multi-task, Multi-domain Learning

International audienceWe present an approach that leverages multiple datasets possibly annotated using different classes to improve the semantic segmentation accuracy on each individual dataset. We propose a new selective loss function that can be integrated into deep networks to exploit training data coming from multiple datasets with possibly different tasks (e.g., different label-sets). We show how the gradient-reversal approach for domain adaptation can be used in this setup. Thorought experiments on semantic segmentation applications show the relevance of our approach

Xenobiotic CAR activators induce Dlk1-Dio3 locus non-coding RNA expression in mouse liver

Predicting the impact of human exposure to chemicals such as pharmaceuticals and agrochemicals requires the development of reliable and predictive biomarkers suitable for the detection of early events potentially leading to adverse outcomes. In particular, drug-induced non-genotoxic carcinogenesis (NGC) during preclinical development of novel therapeutics intended for chronic administration in humans is a major challenge for drug safety. We previously demonstrated Constitutive Androstane Receptor (CAR) and WNT signaling-dependent up-regulation of the pluripotency associated Dlk1-Dio3 imprinted gene cluster non-coding RNAs (ncRNAs) in the liver of mice treated with tumorpromoting doses of phenobarbital (PB). Here, to explore the sensitivity and the specificity of this candidate liver tumor promotion ncRNAs signature we compared phenotypic, transcriptional and proteomic data from wild-type, CAR/PXR double knock-out and CAR/PXR double humanized animals treated with tumor-promoting doses of PB or chlordane, both well-established CAR activators. We further investigated selected transcriptional profiles from mouse liver samples exposed to seven NGC compounds working through different mode of actions, overall suggesting CAR-activation specificity of the Dlk1-Dio3 long ncRNAs activation. We propose that Dlk1-Dio3 long ncRNAs up-regulation is an early CAR-activation dependent transcriptional signature during xenobiotic-induced mouse liver tumor promotion. This signature may further contribute mode of action-based ‘weight of evidence’ cancer risk assessment for xenobiotic-induced rodent liver tumors

Sensorimotor computation underlying phototaxis in zebrafish

Animals continuously gather sensory cues to move towards favourable environments. Efficient goal-directed navigation requires sensory perception and motor commands to be intertwined in a feedback loop, yet the neural substrate underlying this sensorimotor task in the vertebrate brain remains elusive. Here, we combine virtual-reality behavioural assays, volumetric calcium imaging, optogenetic stimulation and circuit modelling to reveal the neural mechanisms through which a zebrafish performs phototaxis, i.e. actively orients towards a light source. Key to this process is a self-oscillating hindbrain population (HBO) that acts as a pacemaker for ocular saccades and controls the orientation of successive swim-bouts. It further integrates visual stimuli in a state-dependent manner, i.e. its response to visual inputs varies with the motor context, a mechanism that manifests itself in the phase-locked entrainment of the HBO by periodic stimuli. A rate model is developed that reproduces our observations and demonstrates how this sensorimotor processing eventually biases the animal trajectory towards bright regions

Drug-induced chromatin accessibility changes associate with sensitivity to liver tumor promotion

Liver cancer susceptibility varies amongst humans and between experimental animal models due to multiple genetic and epigenetic factors. The molecular characterization of such susceptibilities has the potential to enhance cancer risk assessment of xenobiotic exposures and disease prevention strategies. Here, using DNase I hypersensitivity mapping coupled with transcriptomic profiling, we investigate perturbations in cis-acting gene regulatory elements associated with the early stages of phenobarbital (PB)- mediated liver tumor promotion in susceptible versus resistant mouse strains (B6C3F1 versus C57BL/6J). Integrated computational analyses of strain-selective changes in liver chromatin accessibility underlying PB-response reveal differential epigenetic regulation of molecular pathways associated with PB-mediated tumor promotion, including Wnt/-catenin signalling. Complementary transcription factor motif analyses reveal mouse strain-selective gene regulatory networks and a novel role for Stat, Smad and Fox transcription factors in the early stages of PB-mediated tumor promotion. Mapping perturbations in cis-acting gene regulatory elements provides novel insights into the molecular basis for susceptibility to xenobiotic-induced rodent liver tumor promotion and has the potential to enhance mechanism-based cancer risk assessments of xenobiotic exposures

Nephronophthisis

Nephronophthisis (NPH) is an autosomal recessive disease characterized by a chronic tubulointerstitial nephritis that progress to terminal renal failure during the second decade (juvenile form) or before the age of 5 years (infantile form). In the juvenile form, a urine concentration defect starts during the first decade, and a progressive deterioration of renal function is observed in the following years. Kidney size may be normal, but loss of corticomedullary differentiation is often observed, and cysts occur usually after patients have progressed to end-stage renal failure. Histologic lesions are characterized by tubular basement membrane anomalies, tubular atrophy, and interstitial fibrosis. The infantile form is characterized by cortical microcysts and progression to end-stage renal failure before 5 years of age. Some children present with extrarenal symptoms: retinitis pigmentosa (Senior-Løken syndrome), mental retardation, cerebellar ataxia, bone anomalies, or liver fibrosis. Positional cloning and candidate gene approaches led to the identification of eight causative genes (NPHP1, 3, 4, 5, 6, 7, 8, and 9) responsible for the juvenile NPH and one gene NPHP2 for the infantile form. NPH and associated disorders are considered as ciliopathies, as all NPHP gene products are expressed in the primary cilia, similarly to the polycystic kidney disease (PKD) proteins