Christine Cavaro-Ménard

Diagnosis of different liver fibrosis characteristics by blood tests in non-alcoholic fatty liver disease.

Aims: Our aim was to develop an accurate, non‐invasive, blood‐test‐based method for identifying the main characteristics of liver fibrosis in non‐alcoholic fatty liver disease (NAFLD).

Noninvasive liver steatosis quantification using Mri techniques combined with blood markers

Aims To evaluate the accuracy of different techniques of MRI steatosis quantification, based on histological grading and quantification of liver steatosis. Patients and methods Twenty-three patients (21 with nonalcoholic fatty liver disease and two controls) were included. Steatosis was evaluated in liver specimens using histological grading (five grades) and steatosis area (% of liver surface) was computed using an inhouse automated image analysis. The following five MRI quantification techniques were performed: two-point Dixon, three-point Dixon, DUAL, spin echo method and a new technique called multi-echo gradient-echo (MFGRE). Interobserver (two observers) and intersite (three different liver sites) agreements were evaluated for the two best-performing methods. Results Steatosis area was correlated with steatosis grade: Rs (Spearman coefficient)=0.82, P value of less than 0.001. The steatosis area was significantly different between S0–S2 and S3–S4 grades: 4.2±2.4 versus 16.4±8.9% (P<0.001). Correlations between the MRI techniques and steatosis area (or grading) were: MFGRE, Rs=0.72 (0.78); spin echo method, Rs=0.72 (0.76); DUAL, Rs=0.71 (0.76); two-point Dixon, Rs=0.71 (0.75); three-point Dixon, Rs=0.67 (0.77). Interobserver (Ric=0.99) and intersite (Ric=0.97) agreements were excellent for the liver steatosis measurement by MFGRE. The noninvasive diagnosis of the steatosis area was improved by adding blood markers like ALT and triglycerides to MFGRE (aR2: 0.805). Conclusion MRI, and in particular the MFGRE method, provides accurate and automatic quantification for the noninvasive evaluation of liver steatosis, either as a single measurement or in combination with blood variables.

Diagnostic quality assessment of medical images: Challenges and trends

With medical imaging technologies growth, the question of their assessment on the impact and benefit on patient care is rising. Development and design of those medical imaging technologies should take into account the concept of image quality as it might impact the ability of practicians while they are using image information. Towards that goal, one should consider several human factors involved in image analysis and interpretation, e.g. image perception issues, decision process, image analysis pipeline (detection, localization, characterization…). While many efforts have been dedicated to objectively assess the value of imaging system in terms of ideal decision process, new trends have recently emerged to deal with human observer perfomances. This task effort is huge considering the variability of imaging acquisition methods and the possible pathologies. This paper proposes a survey of some key issues and results associated to this effort. We first outline the wide range of medical images with their own specific features. Next, we review the main methodologies to evaluate diagnostic quality of medical images from subjective assessment including ROC analysis, and diagnostic criteria quality analysis, to objective assessment including metrics based on the HVS, and model observers. At last, we present another evaluation method: eye-tracking studies to gain basic understanding of the visual search and decision-making process.

Fuzzy algorithms to extract vacuoles of steatosis on liver histological color images

In this paper, we present an automatic, robust and reliable process to quantify liver steatosis. The degree of steatosis is a useful marker of steatohepatitis. This degree is routinely assessed visually by an expert and then lacks of accuracy and robustness. The process that we have developed is divided in two steps. A fuzzy classification first merges into classes pixels according to their intensity. We use a generalized objective function that allows to detect micro and blurredness vacuoles of steatosis. Then, regions with inhomogeneous texture and irregular shape were eliminated with compactness and standard deviation parameters. The obtained results are good correlated with expert graduation (in five levels). A better correlation is obtained with a more precise grading.

Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size

As a task-based approach for medical image quality assessment, model observers (MOs) have been proposed as surrogates for human observers. While most MOs treat only signal-known-exactly tasks, there are few studies on signal-known-statistically (SKS) MOs, which are clinically more relevant. In this paper, we present a new SKS MO named channelized joint detection and estimation observer (CJO), capable of detecting and estimating signals with unknown amplitude, orientation, and size. We evaluate its estimation and detection performance using both synthesized (correlated Gaussian) backgrounds and real clinical (magnetic resonance) backgrounds. The results suggest that the CJO has good performance in the SKS detection-estimation task.

Steatosis degree, measured by morphometry, is linked to other liver lesions and metabolic syndrome components in patients with NAFLD.

Background and aim We carried out morphometric measurements of steatosis to evaluate relationships between steatosis degree and other liver lesions or metabolic syndrome components in nonalcoholic fatty liver disease (NAFLD). Patients and methods We developed an algorithm to measure steatosis area. Two hundred and fourteen patients with NAFLD were included in derivation (10) and validation (204) groups. Controls consisted of patients who were steatosis-free (12), patients with chronic hepatitis C (188), and patients with alcoholic chronic liver disease (94). Results Accuracy of steatosis area was considered as good or very good in at least 72% of cases by three pathologists. Steatosis areas were as follows: NAFLD=10.3±9.7%, virus=2.4±3.1%, alcohol=7.8±8.2% (P<0.0001). Steatosis area was closely related to steatosis grades in NAFLD (P<0.0001 for linear trend). Steatosis area increased from the fibrosis stage F0 to the fibrosis state F2, then decreased in the stages F3 and F4 (cirrhosis) (P<0.0001 for quadratic trend). Fibrosis was present in an average steatosis area of approximately 4% (defining significant steatosis) and in nonalcoholic steatohepatitis by approximately 8% (defining severe steatosis). Steatosis and fibrosis area increased symmetrically until approximately 10%, then steatosis area decreased to null as average fibrosis area reached 32%. Average fasting glycemia (approximately 92 mg/dl) or triglycerides and BMI plateaued before a steatosis area of approximately 4%, then increased thereafter. Significant steatosis was present in 61.3% of NAFLD versus 20.2% of viral hepatitis (P<0.0001) and in 58.7% of alcoholic liver diseases (P=0.674). Conclusions The average threshold of steatosis area is 4% for the development of fibrosis or metabolic syndrome components and 8% for nonalcoholic steatohepatitis. Steatosis area may contribute to defining the normal range and clinical course of metabolic components.

A multi-slice model observer for medical image quality assessment

Model observers (MOs) have been developed for the medical image quality assessment. Nowadays, numerous modern medical instruments are capable of producing 3D images, while few researchers have conducted MO studies on 3D data. In this paper, we propose a multi-slice MO when considering a relatively more realistic diagnostic task: the detection-localization of simulated multiple-sclerosis (MS) lesions on 3D magnetic resonance (MR) images. The jackknife free-response receiver operating characteristic (JAFROC) method was used to quantitatively analyse its performances and compare them with those of human observers. Our preliminary results showed that the proposed framework has the potential to approach human detection-localization task performance.

Eye-position recording during brain MRI examination to identify and characterize steps of glioma diagnosis

MRI is an essential tool for brain glioma diagnosis thanks to its ability to produce images in any layout plan and to its numerous sequences adapted to both anatomic and functional imaging. In this paper, we investigate the use of an eyetracking system to explore relationships between visual scanning patterns and the glioma diagnostic process during brain MRI analysis. We divide the analyzed screen into Areas of Interest (AOIs), each AOI corresponding to one sequence. Analyzing temporal organization of fixation location intra AOI and inter AOI splits the diagnostic process into different steps. The analysis of saccadic amplitudes reveals clear delineation of three sequential steps. During the first step (characterized by large saccades), a radiologist performs a short review on all sequences and on the patient report. In the second step (characterized by short saccades), a radiologist sequentially and systematically scans all the slices of each sequence. The fixation duration in one AOI depends on the number of slices, on the lesion subtlety and on the lesion contrast in the sequence to be analyzed. In order to improve the detection, localization and characterization of the glioma, the radiologist compares sequences during the third step (characterized by large saccades). Eye-position recording enables one to identify each elementary task implemented during diagnostic process of glioma detection and characterization on brain MRI. Total dwell time associated with one MRI sequence (one AOI) and contrast in primary lesion area enable one to estimate the amount and subtleties of diagnosis criteria provided by the sequence. From this information, one could establish some rules to optimize brain MRI compression (depending on the sequence to be compressed).

Fuzzy algorithms: Application to adipose tissue quantification on MR images☆

Abstract Metabolic syndrome, which is related to abdominal obesity, is a fast growing disease in our western countries. Its presence greatly increases the risk of developing cardiovascular diseases. The accumulation of visceral adipose tissue plays a key role in the development of the metabolic syndrome. The increase of waist circumference is one of the five criteria of the metabolic syndrome diagnosis. But this increase can be due to visceral or subcutaneous adipose tissues. And these adipose tissues do not play the same rule in metabolic syndrome. The purpose of this study was to develop software for automatic and reliable quantification of visceral and subcutaneous adipose tissues, to detect patient with high risk to develop metabolic syndrome and to follow the evolution of adipose tissue repartition after treatment. A gradient echo magnetic resonance (MR) technique is used, with a TE such that fat and water are opposed in phase. The developed process is based on two fuzzy algorithms. First, we fuzzy generalized clustering algorithms allow to merge pixels according to their intensities. Then, fuzzy connectedness algorithm allows to merge pixels according to cost function related to distance, gradient distance and intensities. A validation is performed with a comparison between expert results made by manual drawing and purpose-made software results. Our software provides an automatic and reliable method to segment visceral and subcutaneous adipose tissue and additionally avoids in some case the problem of inhomogeneity of signal intensity.

QoE for telemedicine: challenges and trends

Telemedicine that involves sharing of digital data (i.e. physiological signals, 2D/3D images and videos) should meet the same standards of traditional healthcare in terms of usability, accessibility, efficiency, effectiveness and quality of clinical processes. All these requirements can be seen as elementary components that participate to the quality of experience (QoE) in an ad hoc medical application scenario. Although, the quality of service (QoS) in telemedicine has been quite investigated, QoE is still lacking clear definition in this context. This later should not be assimilated to QoS as it refers clearly to the experience by the user while QoS focuses mostly on the system. QoE has a potential relevancy to optimize and understand the technical transmission chain from the final task point viewpoint which one of the most important factor for adoption of telemedicine. Towards this goal, QoE studies should be conducted with an appropriate methodology incorporating user involvement and digital data and their relation with QoS. As one of the first effort in this field, this paper proposes a survey of some key issues and solutions associated to QoE in telemedicine. We first present the services offered by telemedicine and underline the significance of QoE for tele-diagnosis and tele-surgery. Next, we identify and analyze the influencing factors such as application area, application purpose (emergency care, acquisition assistance, second opinion, education...), content type (data specificities depending on acquisition modality), context of use (offline/real time, interactivity...), and users state (stress, expertise...), that have to be considered for a relevant QoE assessment in telemedicine.