Automatic evolutionary medical image segmentation using deformable models

International audienceThis paper describes a hybrid level set approach to medical image segmentation. The method combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation. The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six state-of-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed

Bayesian Atlas Estimation for the Variability Analysis of Shape Complexes

International audienceIn this paper we propose a Bayesian framework for multi-object atlas estimation based on the metric of currents which permits to deal with both curves and surfaces without relying on point correspondence. This approach aims to study brain morphometry as a whole and not as a set of different components, focusing mainly on the shape and relative position of different anatomical structures which is fundamental in neuro-anatomical studies. We propose a generic algorithm to estimate templates of sets of curves (fiber bundles) and closed surfaces (sub-cortical structures) which have the same " form " (topology) of the shapes present in the population. This atlas construction method is based on a Bayesian framework which brings to two main improvements with respect to previous shape based methods. First, it allows to estimate from the data set a parameter specific to each object which was previously fixed by the user: the trade-off between data-term and regularity of deformations. In a multi-object analysis these parameters balance the contributions of the different objects and the need for an automatic estimation is even more crucial. Second, the covariance matrix of the deformation parameters is estimated during the atlas construction in a way which is less sensitive to the outliers of the population

Towards joint morphometry of white matter tracts and gray matter surfaces

Organization for Human Brain Mapping 2013International audienceMorphological studies of anatomical structures play an important role in neuroimaging. For example in the analysis of the circuits connecting sub-cortical areas to the cortical surface one would like to study together gray matter surfaces and white matter tracts (fiber bundles). To the best of our knowledge, there is no generic method to study these objects together. Atlas construction is based on the joint estimate of a common ''mean shape'' (template) and of its deformation towards each shape of the population. The atlas shows the invariants of the population and their variability. We propose a new atlas construction method based on currents [1] which permits to deal with curves and surfaces together. We extend to curves the methodology proposed in [3] for surfaces. This allows to fix a topologically correct representation of the bundle templates. We are therefore able to compare the templates directly with the shapes of the population and to study the relative positions of different objects. Moreover every deformation is based on one single diffeomorphism of the whole 3D space which preserves the spatial organization of the objects making possible a joint analysis of multiple objects

Unified analysis of shape and structural connectivity of neural pathways

International audienceAn abnormal brain development due to a neuropsychiatric disorder can influence the shape and the anatomical organization of both white and grey matter structures. An example is the syndrome of Gilles de la Tourette (GTS) which is thought to be associated with dysfunctions of the cortico-striato-pallido-thalamic circuits [6]. These anatomical complexes should be studied as a whole, analysing both the shape and the relative position of their structures.Atlas constructions permit to estimate an average shape complex of a given population, called template, and its deformations towards the shape complexes of each subject. The template represents the morphological invariants of the population whereas the deformations capture its variability.Previous works defined these deformations as single diffeomorphisms acting on the entire 3D space, so that ending points of fiber bundles could not move independently of grey matter structures [1,2,4,5]. This implicitly assumes that fiber bundles connect the same areas of grey matter structures across subjects. This assumption is not compatible with the aforementioned hypothesis about GTS [6] which relates the syndrome to atypical configurations of neural circuits. We propose a new atlas construction method which can handle both fibers and surfaces and which is based on a double diffeomorphism. This permits to analyse the morphological variations of each structure and the changes in the relative position between fiber bundles and grey matter structures, namely the variations in structural connectivity

Robust imaging of hippocampal inner structure at 7T: in vivo acquisition protocol and methodological choices

International audienceOBJECTIVE:Motion-robust multi-slab imaging of hippocampal inner structure in vivo at 7T.MATERIALS AND METHODS:Motion is a crucial issue for ultra-high resolution imaging, such as can be achieved with 7T MRI. An acquisition protocol was designed for imaging hippocampal inner structure at 7T. It relies on a compromise between anatomical details visibility and robustness to motion. In order to reduce acquisition time and motion artifacts, the full slab covering the hippocampus was split into separate slabs with lower acquisition time. A robust registration approach was implemented to combine the acquired slabs within a final 3D-consistent high-resolution slab covering the whole hippocampus. Evaluation was performed on 50 subjects overall, made of three groups of subjects acquired using three acquisition settings; it focused on three issues: visibility of hippocampal inner structure, robustness to motion artifacts and registration procedure performance.RESULTS:Overall, T2-weighted acquisitions with interleaved slabs proved robust. Multi-slab registration yielded high quality datasets in 96 % of the subjects, thus compatible with further analyses of hippocampal inner structure.CONCLUSION:Multi-slab acquisition and registration setting is efficient for reducing acquisition time and consequently motion artifacts for ultra-high resolution imaging of the inner structure of the hippocampus

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Genetic Algorithms for Voxel-based Medical Image Registration

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Biomedical image segmentation using geometric deformable models and metaheuristics

International audienceThis paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities