Special Issue on biomedical image segmentation using variational and statistical approaches
type: Dataset
description: Technological advances in medical imaging have been extremely fast over the last decades, making human observation possible at different scales from molecular to cellular to organ even the whole body. According to the modality, imaging is able to furnish morphological, structural, metabolic and functional information, which can be conjointly exploited to improve the understanding of the disease and assist the medical expert in the decision-making. As a consequence, usage of medical imaging has greatly increased to play today a key role in clinical practice in all phases of the management of the disease. These include prediction, diagnosis, therapy planning, therapy guidance, intervention guidance, monitoring… However, a major challenge is how to deal with the big amount of available data, interpret and exploit them in order to really bring a valuable help to medical experts whether in the decision-making or for improving the procedure safety and outcome. To deal with the technological problems of image analysis, computer aided systems have been developing for advanced applications dedicated to image guided diagnosis (CAD), therapy and intervention, including planning treatment. However, the complexity of image content, their size and their number impose to design processing tools that meet the clinical requirements (accuracy, robustness, and computation time). Among these tools, the segmentation appears as a prerequisite and a key step for all these applications. A lot of work have been devoted to this segmentation and continue to be developed. Despite advances in the mathematical tools as well in the image resolution, segmentation still remains an open problem. The challenges of medical image segmentation are often related to the low contrast, the presence of noise and different kinds of artefacts (presence of extrinsic objects, motion…) but also to the shape complexity of the structure of interest, its contrast and geometry variability over time or in the course o…
issued: 2026-05-06T00:43:57.316251
language: en
modified: 2026-05-06T00:43:57.316256
contact point: Organization
  fn: CCSD
keyword: infoeu-reposemanticsother
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: special-issue
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
landing page: https://hal.science/hal-00959942

Special Issue on biomedical image segmentation using variational and statistical approaches
type: Distribution
format: HTML
issued: 2026-05-06T00:43:57.319786
modified: 2026-05-06T00:43:57.309386
access URL: https://hal.science/hal-00959942

test_moissonnage_selune
type: Agent