3D landmark detection for augmented reality based otologic procedures

International audienceEar consists of the smallest bones in the human body and does not contain significant amount of distinct landmark points that may be used to register a preoperative CT-scan with the surgical video in an augmented reality framework. Learning based algorithms may be used to help the surgeons to identify landmark points. This paper presents a convolutional neural network approach to landmark detection in preoperative ear CT images and then discusses an augmented reality system that can be used to visualize the cochlear axis on an otologic surgical video

Utilisation de l'intelligence artificielle pour les procédures de curiethérapie prostatique guidée par l'image

Radiotherapy procedures aim at exposing cancer cells to ionizing radiation. Permanently implanting radioactive sources near to the cancer cells is a typical technique to cure early-stage prostate cancer. It involves image acquisition of the patient, delineating the target volumes and organs at risk on different medical images, treatment planning, image-guided radioactive seed delivery, and post-implant evaluation. Artificial intelligence-based medical image analysis can benefit radiotherapy procedures. It can help to facilitate and improve the efficiency of the procedures by automatically segmenting target organs and extrapolating clinically relevant information. However, manual delineation of target volumes is still the standard routine for most clinical centers, which is time-consuming, challenging, and not immune to intra- and inter-observer variations. In this thesis, we aim to develop medical image processing solutions to automate various components of the current image-guided prostate brachytherapy procedures, including radioactive seeds identification from CT images and clinical target volume segmentation from different medical images. In the first application, we developed and evaluated a new technique for detecting and identifying implanted radioactive seeds on post-implant CT scans of prostate brachytherapy. This allows experts to evaluate the quality of the image-guided radioactive seed delivery by computing the delivered dosimetric parameters, specifically to compute the post-implant dosimetry of salvage prostate brachytherapy performed years after primary brachytherapy in the treatment of relapsed prostate cancer. The second application involved the development of deep learning methods to delineate clinical target volumes automatically. We evaluated the proposed methods on a clinical database of intraoperative transrectal ultrasound and post-implant CT images of image-guided prostate brachytherapy. The evaluation is then extended to other medical image analysis applications. Our methods yielded promising results and opening important perspectives towards efficient and accurate medical image analysis tasks. They can be applied to automate the management of image-guided prostate brachytherapy procedures.Les procédures de radiothérapie visent à exposer les cellules cancéreuses aux rayonnements ionisants. L'implantation permanente de sources radioactives à proximité des cellules cancéreuses est une technique classique pour guérir le cancer de la prostate à un stade précoce. Le processus implique l'acquisition d'images du patient, la délimitation des volumes cibles et des organes à risque à l'aide de l'imagerie, la planification du traitement, l’implantation de grains radioactifs guidées par l'image et l'évaluation post-implantatoire. L'analyse d'images médicales basée sur l'intelligence artificielle peut être bénéfique pour des procédures de radiothérapie. Elle peut aider à faciliter et à améliorer l'efficacité des procédures en segmentant automatiquement les organes cibles les images et en extrapolant des informations cliniquement pertinentes. Cependant, la délimitation manuelle des volumes cibles est toujours la routine standard pour la plupart des centres cliniques, ce qui prend du temps et n'est pas à l'abri de variations intra et inter-observateurs. Dans cette thèse, nous visons à développer des solutions de traitement d'images médicales pour automatiser divers étapes des procédures actuelles de curiethérapie de la prostate guidée par l'image, notamment l'identification des grains radioactifs à partir d'images de scanner X et la segmentation du volume cible clinique à partir d'images médicales.Dans la première application, nous avons développé et évalué une nouvelle technique de détection et d'identification des grains radioactifs implantés sur des scanner X post-implantatoire en rapport avec la curiethérapie prostatique. Cela permet aux experts d'évaluer la qualité du positionnement de grains radioactifs guidées par l'image en calculant les paramètres dosimétriques, en particulier le calcul de dosimétrie post-implantoire de la curiethérapie de rattrapage de la prostate réalisée des années après la curiethérapie initiale dans le cadre de récidive de cancer de la prostate. La deuxième application impliquait le développement de méthodes d'apprentissage profond pour délimiter automatiquement les volumes cibles cliniques. Nous avons évalué les méthodes proposées sur une base de données cliniques d'échographie transrectale peropératoire et des images scanner X post-implantoires de la curiethérapie prostatique guidée par l'image. L'évaluation de notre méthode a été ensuite étendue à d'autres applications d'analyse d'images médicales. Nos méthodes ont donné des résultats prometteurs menant à une perspective essentielle pour des tâches d'analyse d'images médicales efficaces et précises. Elles peuvent être rebuvant être appliquées pour automatiser la gestion des procédures de curiethérapie prostatique guidée par l'image

Fast interactive medical image segmentation with weakly supervised deep learning method

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A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy

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Augmented reality for inner ear procedures: visualization of the cochlear central axis in microscopic videos

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Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network

International audienceAbstract Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved performance can be achieved by registering patient-specific CT images to a high-resolution inner ear model built from accurate 3D segmentations based on micro-CT of human temporal bone specimens. This paper presents a framework based on convolutional neural network for human inner ear segmentation from micro-CT images which can be used to build such a model from an extensive database. The proposed approach employs an auto-context based cascaded 2D U-net architecture with 3D connected component refinement to segment the cochlear scalae, semicircular canals, and the vestibule. The system was formulated on a data set composed of 17 micro-CT from public Hear-EU dataset. A Dice coefficient of 0.90 and Hausdorff distance of 0.74 mm were obtained. The system yielded precise and fast automatic inner-ear segmentations

Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy

International audienceDeep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven multimodal prostate segmentation method using convolutional neural network (DGMNet). The novelty of our method comes with its embedded generative neural network for learning-based shape modeling and its ability to adapt for different imaging modalities via learning-based registration. The proposed method includes a multi-task learning framework that combines a convolutional feature extraction and an embedded regression and classification based shape modeling. This enables the network to predict the deformable shape of an organ. We show that generative neural network-based shape modeling trained on a reliable contrast imaging modality (such as MRI) can be directly applied to low contrast imaging modality (such as CT) to achieve accurate prostate segmentation. The method was evaluated on MRI and CT datasets acquired from different clinical centers with large variations in contrast and scanning protocols. Experimental results reveal that our method can be used to automatically and accurately segment the prostate gland in different imaging modalities

Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI Using Deep Convolutional Networks

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Use of Super Paramagnetic Iron Oxide Nanoparticles as Drug Carriers in Brain and Ear: State of the Art and Challenges

International audienceDrug delivery and distribution in the central nervous system (CNS) and the inner ear represent a challenge for the medical and scientific world, especially because of the blood–brain and the blood–perilymph barriers. Solutions are being studied to circumvent or to facilitate drug diffusion across these structures. Using superparamagnetic iron oxide nanoparticles (SPIONs), which can be coated to change their properties and ensure biocompatibility, represents a promising tool as a drug carrier. They can act as nanocarriers and can be driven with precision by magnetic forces. The aim of this study was to systematically review the use of SPIONs in the CNS and the inner ear. A systematic PubMed search between 1999 and 2019 yielded 97 studies. In this review, we describe the applications of the SPIONS, their design, their administration, their pharmacokinetic, their toxicity and the methods used for targeted delivery of drugs into the ear and the CNS

Stereo calibration of non-overlapping field of view heterogeneous cameras for calibrating surgicalmicroscope with external tracking camera

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