Documents et medias

Publications scientifiques

Publications scientifiques

Mis-selection of non-malignant lesions as target lesions: Misclassification of RECIST 1.1 and Early Termination of Promising Drugs?

Antoine Iannessi [1], Hubert Beaumont [1], Yan Liu [1], Anne Sophie Bertrand [2] - Affiliations: [1] Median Technologies, Valbonne, France, [2] Polyclinique Les Fleurs, 83190 Ollioules, France

Poster presented at the ESMO 2020 Virtual Congress | September 17, 2020 |Poster Display Session 1936P

For tumor response assessment in oncology trials with radiology, the baseline (BL) evaluation is critical as the selection of target lesions (TL) determines the quality of follow-up. The RECIST workgroup provided a method and recommendations for: 1) selecting TL and non-target lesions (NTL) for reporting disease evolution, 2) choosing up to 5 targets, with a maximum of two per organs. In the practice, the selection of TL is subjective; non-malignant (NM) lesions might be mistaken as TLs. The objectives of the study are 1) to analyze the impact of selecting non-malignant lesions as target lesions at baseline, and 2) to present recommendations to mitigate risks in clinical trials.

Mask uncertainty regularization to improve machine learning based medical image segmentation

Vladimir Groza [1], Benoit Huet [1], Nozha Boujemaa [1] - Affiliations : [1] Median Technologies, Valbonne, France

Poster presented at the IEEE International Symposium on Biomedical Imaging (ISBI) | April 3-7, 2020 | Virtual conference.

Segmentation of the different body structures on CT and MRI scans remains a challenging problem that requires accurate ground truth (GT) segmentation. One of the important aspects is the lack of reliability caused by radiologists annotation disagreement coupled with insufficient quality of the medical images. An independent multiple annotation is needed to overcome frequent disagreements between radiologists decisions mostly on the organs border, which is always blurred and affects on the providing an accurate segmentaKon even on contrasted CT/MRI images. Augmentation techniques aim at increasing model generalization by manipulating inputs in order to enrich set of the variability of images in regards to their GT. We introduce a method to regularize the models learning process by augmenting the information only in the associated GT masks.

Pneumothorax segmentation with effective conditioned post-processing in the chest X-Ray

Vladimir Groza [1], Artur Kuzin [2] - Affiliations: [1] MedianTechnologies, Valbonne, France. [2] X5 Retail Group, Russia. Corresponding author e-mail:

Poster presented at the International IEEE conference on Biomedical Imaging | April 3-7, 2020 | Virtual conference.
The pneumothorax can be caused by a blunt chest injury, damage from underlying lung disease or it may occur for no obvious reason at all. This is one of the complex problems for the experts manual detection, which can be solved automaHcally and simplify the clinical workflow. In several situations, lung collapse can turn out as serious threat to life.Proposed method presents new segmentaHon pipeline for the chest X-ray images with the multi step conditioned postprocessing.This approach leads to the significant improvement compare with any « baseline » by the reduction of the totally missed and false positive detections of the pneumothorax collapse regions.

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:

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:

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.

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