Automatic segmentation of the sphenoid sinus in CT-scans volume with DeepMedics 3D CNN architecture

Today, researchers are increasingly using manual, semi-automatic, and automatic segmentation techniques to delimit or extract organs from medical images. Deep learning algorithms are increasingly being used in the area of medical imaging analysis. In comparison to traditional methods, these algorithms are more efficient to obtain compact information, which considerably enhances the quality of medical image analysis system. In this paper, we present a new method to fully automatic segmentation of the sphenoid sinus using a 3D (convolutional neural network). The scarcity of medical data initially forced us through this study to use a 3D CNN model learned on a small data set. To make our method fully automatic, preprocessing and post processing are automated with extraction techniques and mathematical morphologies. The proposed tool is compared to a semi-automatic method and manual deductions performed by a specialist. Preliminary results from CT volumes appear very promising

IRM cérébrale de 26 patients guadeloupéens présentant une paralysie supranucléaire progressive

DIJON-BU Médecine Pharmacie (212312103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Recherche systématique des anévrysmes intracrâniens en IRM sur une population atteinte de polykystose rénale autosomique dominante (134 patients)

DIJON-BU Médecine Pharmacie (212312103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Residual or Retained Gadolinium

International audienc

A Segmentation Method of Skin MRI 3D High Resolution in vivo

Background: In recent years, Magnetic Resonance Imaging (MRI) is used in clinical application as non-invasive medical modality, it is rarely used to study the anatomy physiological, and biochemical of the skin, in spite of its very attractive modality for skin imaging. It makes an ideal imaging modality of unique soft tissue contrast to study the skin water content and to differentiate between the different skin layers. However MRI provides a big data with high quality. The analysis of these data require computerized methods to help clinicians and to improve disease of diagnosis. Several image processing method have been extensively used to assist doctors in qualitative diagnosis, segmentation is one of the most methods used in medical image processing for many applications in order to understand medical data and extract useful information. The purpose of this study is to use the segmentation method to measure the hydration of skin using MRI modality. Methods: We will classify segmentation approaches for MRI data into three basics classes: Edge based segmentation, Region based segmentation, and Thresholding segmentation. Then we will briefly describe Fuzzy C-means Clustering method. Furthermore, we will give some related works used FCM algorithm with MRI images. Results: We have measured the hydration of the feet as a result of the FCM segmentation method, where the sample of the study was conducted on 35 healthy volunteers, who were scanned by MRI machine before applying moisturizer and one hour after. Conclusion: MRI is an attractive modality to study the skin water content, it makes an ideal observation of the different skin layers in vivo with three dimensions. However, the segmentation of MRI data by FCM clustering is a computerized method to help clinicians in order to measure skin hydration. &nbsp

Imagerie de sources électrophysiologiques par apprentissage supervisé

International audienceImagerie de sources électrophysiologiques par apprentissage supervis

EEG Source Imaging by Supervised Learning

International audienceEstimating the electrophysiological activity at the origin of electroencephalography (EEG) measurements is an ill-posed inverse problem. Several methods solve this problem by imposing different priors on the solution. Machine learning could allow to learn the inverse function directly from the data and thusmake the choice of one of the multiple solutions of the inverse problem more reliable. This work is based on simulations of electrophysiologic data containing single or multiple extended sources, using the SEREEGA simulation toolbox [1]. These data are used to train a one-dimensional convolutional network (1D-CNN) and to compare the results of this learning approach to those obtained by a recurrent long short term memory (LSTM) network from the literature, and by minimum norm energy [2] (MNE) and standardized low resolution brain electromagnetic tomography [3] (sLORETA) methods. These results on simulated data are encouraging about the potential contribution of learning-based methods to the problem of spatio-temporal EEG sourceimaging. Additional work still needs to be done in order to also evaluate the ability of the network to generalize to real data

Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization

International audienceRecent clinical research studies in forensic identification have highlighted the interest of sphenoid sinus anatomical characterization. Their pneumatization, well known as extremely variable in degrees and directions, could contribute to the radiologic identification, especially if dental records, fingerprints or DNA samples are not available. In this paper, we present a new approach for automatic person identification based on sphenoid sinus features extracted from computed tomography (CT) images of the skull. First, we present a new approach for fully automatic 3D reconstruction of the sphenoid hemisinuses which combines the fuzzy c-means method and mathematical morphology operations to detect and segment the object of interest. Second, deep shape features are extracted from both hemisinuses using a Dilated Residual version of a Stacked Convolutional Auto-Encoder. The obtained binary segmentation masks are thus hierarchically mapped into a compact and low-dimensional space preserving their semantic similarity. We finally employ the l2 distance to recognize the sphenoid sinus and therefore identify the person. This novel sphenoid sinus recognition method obtained 100% of identification accuracy when applied on a dataset composed of 85 CT scans stemming from 72 individuals

Artificial neuroradiology: Between human and artificial networks of neurons?

International audienceIncreasingly widespread application of advanced image processing and Artificial Intelligence (AI) in the field of neuroradiology highlights changing trends in the availability of emerging technologies. In the past 10 years, publications on AI in radiology have increased from 100-150 to 700-800 per year and neuroradiology appears as the most involved subspecialty, accounting for about one-third of articles.But it should be noted that the application of lower levels of AI through medical image processing has been integrated since the birth of our discipline. The convergence of better technological performance and higher volume of data to process has favored the development of more advanced processes, such as machine learning (ML).AI can be attributed to any machine performing a task normally claiming human cognition. ML is a type of AI that allows computers to learn from data without explicit programming (not by programming it for a specific domain, but by designing a system that can learn from several examples to solve a problem, such as classification algorithms: clustering, support vector machine.. .). ML-based algorithms may differ depending on the approach, the type of data, and the task. Supervised and unsupervised learning are part of this. In this latter approach neither criteria nor ground truth are used to train the algorithm. Deep learning is therefore a supervised machine learning method that uses a specific architecture, mainly a form of neural network to automatically extract relevant features. These neural networks are inspired by the structure of the brain

Time course of NAA T2 and ADC(w) in ischaemic stroke patients: H-1 MRS imaging and diffusion-weighted MRI

International audienceAbstract: Background and purpose: Proton spectroscopy and quantitative diffusion-weighted imaging (DWI) were used to investigate the pertinence of N-acetyl aspartate (NAA) as a reliable marker of neuronal density in human stroke. Methods: The time Courses of tissue water apparent diffusion coefficient (ADC(w)) and metabolite T2 were investigated on a plane corresponding to the largest area of cerebral infarction, within and outside the site of infarction in 71 patients with a large cortical middle cerebral artery territory infarction. Results: Significant reductions are seen in NAA T2 deep within the infarction during the period comprised between 5 and 20 days postinfarction; the relaxation times appearing to normalise several months after stroke. After an acute reduction in ADC(w), the pseudonormalisation of ADC(w), occurs at 812 days after the ischaemic insult. The minimum in N-acetyl aspartate T2 relaxation times and the pseudonormalisation of ADC, appear to coincide. Conclusions: The data suggest that modifications in the behaviour of the observed proton metabolites occur during the period when the vasogenic oedema is formed and cell membrane integrity is lost. For this reason, NAA may not be a reliable marker of neuronal density during this period