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Scientific publications

Scientific publications

Automatic Fibrosis High Risk Prediction using Computed Tomography Imaging

Elton Rexhepaj [1], Corinne Ramos [1], Nozha Boujemaa [1], Benoit Huet [1] -Affiliations: [1] Median Technologies, Valbonne, France, in col. with: Olivier Lucidarme. Corresponding author e-mail: elton.rexhepaj@mediantechnologies.com

Poster presented at AASLD: The liver meeting 2019 conference | Nov 8-12, 2019 |Boston (USA).

The diagnosis of hepatic fibrosis within liver disease is important for prognosis, stratification for treatment and monitoring of treatment. Although liver biopsy is considered the gold standard for staging fibrosis, it has its limitations due to its invasive nature, sampling error and inter-observer variability. Previous studies have shown that computer tomography (CT) perfusion imaging and splenic radiomics can accurately assess and grade liver fibrosis.

Classification of hepatic tissue in CT: Does IV contrast really matter?

Hubert Beaumont [1] -Affiliations: [1] Median Technologies, Valbonne, France, in col. with: Antoine Iannessi, Fanny Orlhac, Jean-Michel Cucchi. Corresponding author e-mail: hubert.beaumont@mediantechnologies.com

Poster presented at AASLD: The liver meeting 2019 conference | Nov 8-12, 2019 |Boston (USA).

The characterization of biological tissues by means of medical imaging continues to spark a lot of attention, and numerous studies have addressed a broad spectrum of clinical applications while involving most of the imaging modalities. Contrast enhancement products were originally designed for improving human perception, but it is not certain computer assisted analysis still needs it. So far, the impact of IV contrast agents on radiomics-based detection is not fully understood.

The Poincaré-Boltzmann Machine: passing the information between disciplines

Pierre Baudot, PhD [1], Mathieu Bernardi, MscI[1] -Affiliations: [1] Median Technologies, Valbonne, France, in col. with Daniel Bennequin, Monica Tapia, Jean-Marc Goaillard. Corresponding author e-mail: pierre.baudot@mediantechnologies.com

Poster presented at the Geometric Sciences of Information 2019 (GSI2019) conference | Aug 27-29, 2019 |Toulouse (France). Information cohomology is a branch of topological data analysis that allows us to quatify directly statistical dependencies and independences in a given dataset. Theorems establish Shannon entropy as a first cohomology class and mutual information as coboundaries on finite probability space endowed with a random variable chain complex structure. We present some simplicial subcase applications to supervised and unsupervised learning in different contexts: transcriptomic and digits and medical CT image classification.

Imaging biomarker phenotyping system (iBiopsy) to accelerate hepatocellular carcinoma (HCC) drug development

Yan Liu [1], Corinne Ramos [1], Pierre Baudot [1], Johan Brag [1], Olivier Lucidarme [2] - Affiliations: [1] Median Technologies, Valbonne, France. [2] Radiology Unit, Pitié Salpétrière Hospital, APHP, Paris, France.

Current drug therapies in HCC remain limited because of substantial genomic, cellular and molecular heterogeneity of the liver tumor microenvironment (TME). This heterogeneity has implications for tumor development, immune response and cellular invasion and is compounded by multiple molecular pathways related to the various HCC etiologies. In this context, it is challenging to find biomarkers that are predictive of therapeutic response or outcome. A systems biology approach leveraging the iBiopsy phenotyping platform to automatically detect HCC and TME subtypes using non-invasive medical imaging could help identify specific disease pathways and accelerate HCC drug development.

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DUALTAIL-NET for Liver Segmentation on Abdominal CT Images

Vladimir Groza [1], Johan Brag [1], Michael Auffret [1] - Affiliations: [1] Median Technologies, Valbonne, France

Poster presented at The IEEE International Symposium on Biomedical Imaging (ISBI) 2019 | Venice, Italy | April 8-11, 2019
In clinical trials, the evaluation of CT/MRI images is usually done manually or with the use of semi-automatic segmentation techniques. Liver segmentation, as well as segmentations of other organs such as prostate, lung, is a crucial step in computer-aided systems for cancer detection. In order to improve the quality and performance of diagnosis computer-aided systems became popular, where deep learning approach demonstrates its potential and strengths as robust and powerful tool in particular for medical image segmentation. This work presents a novel deep convolutional neural network architecture DualTail-Net in application for automatic liver segmentation on abdominal CT images.

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