Traitement d'images pour l'analyse sémantique des interventions coronariennes en cardiologie

Percutaneous coronary intervention (PCI) is performed using real-time radiographic imaging in an interventional suite. Modeling these ICP procedures to help the practitioner involves understanding the different phases of the ICP procedure, by the interventional machine, which can be used to optimize the X-ray dose and the contrast agent. One of the important tasks in achieving this goal is to segment different interventional tools into the flow of fluoroscopic images and to derive semantic information from them. The component tree, a powerful mathematical morphological tool, forms the basis of the proposed segmentation methods. We present this work in two parts: 1) the segmentation of the low-contrast empty catheter, and 2) the segmentation of the tip of the guide and the monitoring of the detection of the intervention vessel. We present a new multi-scale space-based segmentation method for detecting low-contrast objects such as an empty catheter. For the last part, we present the segmentation of the tip of the guide with filtering based on the component tree and propose an algorithm to semantically follow the segmented tip to determine the intervention vesselL'intervention coronarienne percutanée (ICP) est réalisée en utilisant l'imagerie radiographique en temps réel dans une suite interventionnelle. La modélisation de ces procédures ICP pour aider le praticien implique la compréhension des différentes phases de la procédure ICP, par la machine d’intervention, qui peut être utilisées pour optimiser la dose de rayons X et l'agent de contraste. Pour atteindre cet objectif, l’une des tâches importantes consiste à segmenter différents outils d’intervention dans les flux d’images fluoroscopiques et à en déduire des informations sémantiques. L’arbre des composants, un puissant outil morphologique mathématique, constitue la base des méthodes de segmentation proposées. Nous présentons ce travail en deux parties: 1) la segmentation du cathéter vide à faible contraste, et 2) la segmentation de la pointe du guide et le suivi de la détection du vaisseau d’intervention. Nous présentons une nouvelle méthode de segmentation basée sur l’espace à plusieurs échelles pour détecter des objets faiblement contrastés comme un cathéter vide. Pour la dernière partie, nous présentons la segmentation de la pointe du guide avec le filtrage basé sur l’arbre de composants et proposons un algorithme pour suivre sémantiquement la pointe segmentée pour déterminer le vaisseau d’interventio

VOIDD: automatic vessel of intervention dynamic detection in PCI procedures

International audienceIn this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vessel-of-intervention with accuracy in the range of 88% or above and absence of tip with accuracy in range of 98% or above depending on the test case

Scale-space for empty catheter segmentation in PCI fluoroscopic images

International audiencePurpose: In this article, we present empty guiding catheter segmentation in fluoroscopic X-ray images. The guiding catheter, being a commonly visible landmark, its segmentation is an important brick for Percutaneous Coronary Intervention (PCI) procedure modeling but difficult too. Methods: In number of clinical situations, it is empty and appears as a low contrasted structure with two parallel and partially disconnected edges. To segment it, we work on the level-set scale-space of image, the min tree, to extract curve blobs. We then propose a novel structural scale-space, a hierarchy built on these curve blobs. The deep connected component , i.e. the cluster of curve blobs on this hierarchy, that maximizes the likelihood to be an empty catheter is retained as final segmentation. Results: As a result of evaluation over a database of 1250 fluoroscopic images taken from examinations of 6 patients, we obtain very good qualitative and quantitative segmentation performance, with mean precision and recall of 80.48% and 63.04% respectively. Conclusions: We develop a novel structural scale-space to segment a structured object, the empty catheter in challenging imaging situations where the information content is very sparse. Fully-automatic empty catheter segmentation in X-ray fluoroscopic images is an important and preliminary step in PCI procedure modeling, as it aids in tagging the arrival and removal location of other interventional tools

Nestin as a diagnostic and prognostic marker for combined hepatocellular-cholangiocarcinoma

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Nestin as a Diagnostic and Prognostic Marker for Combined Hepatocellular-Cholangiocarcinoma.

International audienceBACKGROUND AND AIMS: Combined Hepatocellular-Cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) associated with a poor prognosis. Given the challenges in its identification and its clinical implications, biomarkers are critically needed. We aimed to investigate the diagnostic and prognostic value of the immunohistochemical expression of Nestin, a progenitor cell marker, in a large multicentric series of PLC. METHODS: We collected 461 cHCC-CCA samples from 32 different clinical centers. Control cases included 368 hepatocellular carcinomas (HCC) and 221 intrahepatic cholangiocarcinomas (ICCA). Nestin immunohistochemistry was performed on whole tumor sections. Diagnostic and prognostic performances of Nestin expression were determined using receiver operating characteristic curves and cox regression modeling. RESULTS: Nestin was able to distinguish cHCC-CCA from HCC with AUC of 0.85 and 0.86 on surgical and biopsy samples, respectively. Performance was lower for the distinction of cHCC-CCA from ICCA (AUC of 0.59 and 0.60). Nestin, however, showed a high prognostic value, allowing identification of the subset of cHCC-CCA ("Nestin High", >30% neoplastic cells with positive staining) associated with the worst clinical outcome (shorter disease-free and overall survival) after surgical resection and liver transplantation, as well as when assessment was performed on biopsies. CONCLUSION: We show in different clinical settings that Nestin has a diagnostic value and that it is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy. LAY SUMMARY: Combined Hepatocellular-Cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) that lacks robust tissue biomarkers. We show in different clinical settings that Nestin immunohistochemical staining has a diagnostic value and is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy