Place and contribution of tools for the automation of medical image processing in clinical practice

L'application du traitement de l'image et son automatisation dans le domaine de l'imagerie médicale montre l'évolution des tendances avec la disponibilité des technologies émergentes. Les procédés et outils de traitement de l’image médicale sont résumés, les différentes manières de travailler sur une image sont représentés pour expliquer une recherche expansive dans différents domaines, tandis que les applications disponibles sont discutées. Ces applications sont aussi illustrées par le biais d’outils du traitement de l’image développés pour des besoins spécifiques. La catégorisation de chaque travail est effectuée selon des paradigmes. Ces derniers sont définis selon le niveau de considération au niveau global (formation de l’image, amélioration, visualisation, analyse, gestion), au sein de l’image (scène, organe, région, texture, pixel), de l’outil (reconstruction, recalage, segmentation, morphologie mathématique), du processus d’automatisation et de son applicabilité (faisabilité, validation, reproductibilité, implémentation, optimisation) en clinique (prédiction, diagnostic, amélioration, aide à la décision), ou en recherche (niveau de preuve). Par ce biais, il est démontré le rôle de chaque outil pris en exemple dans la construction d’un processus d’automatisation qui est expliqué, et étendu du patient au compte rendu en passant par l’image. L’actualité de la recherche conjointe sur le traitement de l'image et le processus d'automatisation en imagerie médicale actuelle est débattue. Le rôle de la communauté des ingénieurs et radiologues dans et autour de ce processus d’automatisation est discuté.The application of image processing and its automation in the field of medical imaging shows the evolution of trends with the availability of emerging technologies. Medical image processing methods and tools are summarized, different ways of working on an image are represented to explain expansive search in different domains, while available applications are discussed. These applications are also illustrated through image processing tools developed for specific needs. The categorization of each work is done according to paradigms. These are defined according to the level of consideration at the global level (image formation, improvement, visualization, analysis, management), within the image (scene, organ, region, texture, pixel), of the tool (reconstruction, registration, segmentation, mathematical morphology), the automation process and its applicability (feasibility, validation, reproducibility, implementation, optimization) in clinic (prediction, diagnosis improvement, decision support), or in research (level of evidence). In this way, it is demonstrated the role of each tool taken as an example in the construction of an automation process that is explained, and extended from the patient to the report through the image. News from the joint research on image processing and the automation process in current medical imaging is debated.The role of the community of engineers and radiologists in and around this automation process is discussed

Residual or Retained Gadolinium

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Artificial neuroradiology: Between human and artificial networks of neurons?

International audienceIncreasingly widespread application of advanced image processing and Artificial Intelligence (AI) in the field of neuroradiology highlights changing trends in the availability of emerging technologies. In the past 10 years, publications on AI in radiology have increased from 100-150 to 700-800 per year and neuroradiology appears as the most involved subspecialty, accounting for about one-third of articles.But it should be noted that the application of lower levels of AI through medical image processing has been integrated since the birth of our discipline. The convergence of better technological performance and higher volume of data to process has favored the development of more advanced processes, such as machine learning (ML).AI can be attributed to any machine performing a task normally claiming human cognition. ML is a type of AI that allows computers to learn from data without explicit programming (not by programming it for a specific domain, but by designing a system that can learn from several examples to solve a problem, such as classification algorithms: clustering, support vector machine.. .). ML-based algorithms may differ depending on the approach, the type of data, and the task. Supervised and unsupervised learning are part of this. In this latter approach neither criteria nor ground truth are used to train the algorithm. Deep learning is therefore a supervised machine learning method that uses a specific architecture, mainly a form of neural network to automatically extract relevant features. These neural networks are inspired by the structure of the brain

Automated contour detection in spine radiographs and computed tomography reconstructions for forensic comparative identification

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A new T1-weighted SILENZ sequence revealing the bone shape in MRI

International audienceMove towards a solution to be able to simply extract the image of a bone in MRI; and that this image is a reflection of the reality, to be clinically usefu

Model-Based Iterative Reconstruction (MBIR) for ASPECT Scoring in Acute Stroke Patients Selection: Comparison to rCBV and Follow-Up Imaging

Background: To compare a model-based iterative reconstruction (MBIR) versus a hybrid iterative reconstruction (HIR) for initial and final Alberta Stroke Program Early Ct Score (ASPECT) scoring in acute ischemic stroke (AIS). We hypothesized that MBIR designed for brain computed tomography (CT) could perform better than HIR for ASPECT scoring. Methods: Among patients who had undergone CT perfusion for AIS between April 2018 and October 2019 with a follow-up imaging within 7 days, we designed a cohort of representative ASPECTS. Two readers assessed regional-cerebral-blood-volume-ASPECT (rCBV-ASPECTS) on the initial exam and final-ASPECTS on the follow-up non-contrast-CT (NCCT) in consensus. Four readers performed independently MBIR and HIR ASPECT scoring on baseline NCCT. Results: In total, 294 hemispheres from 147 participants (average age of 69.59 ± 15.63 SD) were analyzed. Overall raters’ agreement between rCBV-map and MBIR and HIR ranged from moderate to moderate (κ = 0.54 to κ = 0.57) with HIR and moderate to substantial (κ = 0.52 to κ = 0.74) with MBIR. Overall raters’ agreement between follow-up imaging and HIR/MBIR ranged from moderate to moderate (κ = 0.55 to κ = 0.59) with HIR and moderate to almost perfect (κ = 0.48 to κ = 0.82) with MBIR. Conclusions: ASPECT scoring with MBIR more closely matched with initial and final infarct extent than classical HIR NCCT reconstruction

Edge detector- based automatic segmentation of the skin layers and application to moisturization in high- resolution 3 Tesla magnetic resonance imaging

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Iodine footprint: Moving towards environmental responsibility

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The Combination of Stent and Antiplatelet Therapy May Be Responsible of Parenchymal Magnetic Susceptibility Artifacts after Endovascular Procedure

The aim was to assess the occurrence of magnetic susceptibility artifacts (MSA) following endovascular treatment of intracranial aneurysm by stent using susceptibility weighted imaging (SWI). Imaging and clinical data of 46 patients who underwent stent placement in the case of intracranial aneurysm endovascular treatment (S-Group) were retrospectively analyzed and compared to a control group (C-Group) in which 46 patients had coiling alone. The mean number of MSA was higher in the S-group than in the C-group on postprocedural SWI sequence (8.76, 95%CI [5.76; 11.76] vs. 0.78 [0.32; 1.25], respectively, p < 0.001) with a higher frequency of the appearance of MSA also in the S-group (78.26% vs. 21.74% in the C-group, p < 0.001). In the S-group, in the vascular territory of the treated artery, there was a higher number of MSA than in other vascular territories (mean of 5.18 [3.43; 6.92] vs. 3.08 [1.79; 4.36], p = 0.001). An odds ratio (OR) of 20.98 [5.24; 83.95] suggested a higher proportion of onset of MSA in the S-group than in the C-group (p < 0.001). The appearance of MSA after a treatment by stenting for intracranial aneurysm in patients under antiplatelet therapy was common, particularly in the treated artery territory