Resource library

Scientific publications

Scientific publications

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.

RECIST 1.1 evaluations in a phase II clinical trial: Does reader expertise represent a risk factor for measure reliability?

Beaumont H [1], Evans T [2], Klifa C [1], Hons S [3], Chadjaa M [4], Monostori Z [5], Iannessi A [6] - Affiliations: [1] Median Technologies, Valbonne , France. [2] U. of Pennsylvania, USA. [3] Yonsei University of Medicine, Seoul, South Korea. [4] SANOFI, Vitry sur seine, France. [5] National Koranyi Institute, Budapest, Hungary. [6] Centre Antoine Lacassagne , Nice, France.

Oral presentation at the European Congress of Radiology (ECR) on Feb 27-March 3, 2019, Vienna, Austria

Discrepancies of assessments in a RECIST 1.1 phase II clinical trial – association between adjudication rate and variability in images and tumors selection

Beaumont H [1], Evans T [2], Klifa C [1], Guermazi A [3], Hong S [4], Chadjaa M [5], Monostori Z [6] - Affiliations: [1] Median Technologies, Valbonne, France. [2] Department of medicine, Hospital of the University of Pennsylvania, USA. [3] Quantitative Imaging Center (QIC) Boston University School of Medicine, Boston, USA. [4] Department of Radiology, Severance Hospital Yonsei University of Medicine, Seoul, South Korea. [5] Clinical Research, SANOFI, Paris, France. [6] Radiology, National Koranyi Institute of TB and pulmonology, Budapest, Hungary

In imaging-based clinical trials, it is common practice to perform double reads for each image, discrepant interpretations can result from these two different evaluations. In this study we analyzed discrepancies that occurred between local investigators (LI) and blinded independent central review (BICR) by comparing reader-selected imaging scans and lesions. Our goal was to identify the causes of discrepant declarations of progressive disease (PD) between LI and BICR in a clinical trial.

See publication

High Quality Imaging in Clinical Trials – Standardization mechanisms

Beaumont H - Affiliations: [1] Median Technologies, Valbonne, France

Presentation #18018479 at the Quantitative Imaging Reading Room, Annual Meeting of the Radiology Society of North America (RSNA) 2018; 2018 Nov. 25-29; Chicago, IL, USA

Can we improve cost effectiveness of oncology clinical trials workflow? A prospective RECIST 1.1 study

Hubert Beaumont [1], Antoine Iannessi [2], Catherine Klifa [1], Sebastien Patriti [2] - Affiliations: [1] Median Technologies, Valbonne, France. [2] Centre Antoine Lacassagne, Nice, France.

Poster presented at ESMO ASIA 2018, Singapore, Nov. 23 -25, 2018

Resource library