Robust Bayesian fusion of continuous segmentation maps

International audienceThe fusion of probability maps is required when trying to analyse a collection of image labels or probability maps produced by several segmentation algorithms or human raters. The challenge is to weight the combination of maps correctly, in order to reflect the agreement among raters, the presence of outliers and the spatial uncertainty in the consensus. In this paper, we address several shortcomings of prior work in continuous label fusion. We introduce a novel approach to jointly estimate a reliable consensus map and to assess the presence of outliers and the confidence in each rater. Our robust approach is based on heavy-tailed distributions allowing local estimates of raters performances. In particular, we investigate the Laplace, the Student’s t and the generalized double Pareto distributions, and compare them with respect to the classical Gaussian likelihood used in prior works. We unify these distributions into a common tractable inference scheme based on variational calculus and scale mixture representations. Moreover, the introduction of bias and spatial priors leads to proper rater bias estimates and control over the smoothness of the consensus map. Finally, we propose an approach that clusters raters based on variational boosting, and thus may produce several alternative consensus maps. Our approach was successfully tested on MR prostate delineations and on lung nodule segmentations from the LIDC-IDRI dataset

Altered brain mechanisms of emotion processing in pre-manifest Huntington's disease

Huntington's disease is an inherited neurodegenerative disease that causes motor, cognitive and psychiatric impairment, including an early decline in ability to recognize emotional states in others. The pathophysiology underlying the earliest manifestations of the disease is not fully understood; the objective of our study was to clarify this. We used functional magnetic resonance imaging to investigate changes in brain mechanisms of emotion recognition in pre-manifest carriers of the abnormal Huntington's disease gene (subjects with pre-manifest Huntington's disease): 16 subjects with pre-manifest Huntington's disease and 14 control subjects underwent 1.5 tesla magnetic resonance scanning while viewing pictures of facial expressions from the Ekman and Friesen series. Disgust, anger and happiness were chosen as emotions of interest. Disgust is the emotion in which recognition deficits have most commonly been detected in Huntington's disease; anger is the emotion in which impaired recognition was detected in the largest behavioural study of emotion recognition in pre-manifest Huntington's disease to date; and happiness is a positive emotion to contrast with disgust and anger. Ekman facial expressions were also used to quantify emotion recognition accuracy outside the scanner and structural magnetic resonance imaging with voxel-based morphometry was used to assess the relationship between emotion recognition accuracy and regional grey matter volume. Emotion processing in pre-manifest Huntington's disease was associated with reduced neural activity for all three emotions in partially separable functional networks. Furthermore, the Huntington's disease-associated modulation of disgust and happiness processing was negatively correlated with genetic markers of pre-manifest disease progression in distributed, largely extrastriatal networks. The modulated disgust network included insulae, cingulate cortices, pre- and postcentral gyri, precunei, cunei, bilateral putamena, right pallidum, right thalamus, cerebellum, middle frontal, middle occipital, right superior and left inferior temporal gyri, and left superior parietal lobule. The modulated happiness network included postcentral gyri, left caudate, right cingulate cortex, right superior and inferior parietal lobules, and right superior frontal, middle temporal, middle occipital and precentral gyri. These effects were not driven merely by striatal dysfunction. We did not find equivalent associations between brain structure and emotion recognition, and the pre-manifest Huntington's disease cohort did not have a behavioural deficit in out-of-scanner emotion recognition relative to controls. In addition, we found increased neural activity in the pre-manifest subjects in response to all three emotions in frontal regions, predominantly in the middle frontal gyri. Overall, these findings suggest that pathophysiological effects of Huntington's disease may precede the development of overt clinical symptoms and detectable cerebral atrophy

Mechanisms of TSC-mediated Control of Synapse Assembly and Axon Guidance

Tuberous sclerosis complex is a dominant genetic disorder produced by mutations in either of two tumor suppressor genes, TSC1 and TSC2; it is characterized by hamartomatous tumors, and is associated with severe neurological and behavioral disturbances. Mutations in TSC1 or TSC2 deregulate a conserved growth control pathway that includes Ras homolog enriched in brain (Rheb) and Target of Rapamycin (TOR). To understand the function of this pathway in neural development, we have examined the contributions of multiple components of this pathway in both neuromuscular junction assembly and photoreceptor axon guidance in Drosophila. Expression of Rheb in the motoneuron, but not the muscle of the larval neuromuscular junction produced synaptic overgrowth and enhanced synaptic function, while reductions in Rheb function compromised synapse development. Synapse growth produced by Rheb is insensitive to rapamycin, an inhibitor of Tor complex 1, and requires wishful thinking, a bone morphogenetic protein receptor critical for functional synapse expansion. In the visual system, loss of Tsc1 in the developing retina disrupted axon guidance independently of cellular growth. Inhibiting Tor complex 1 with rapamycin or eliminating the Tor complex 1 effector, S6 kinase (S6k), did not rescue axon guidance abnormalities of Tsc1 mosaics, while reductions in Tor function suppressed those phenotypes. These findings show that Tsc-mediated control of axon guidance and synapse assembly occurs via growth-independent signaling mechanisms, and suggest that Tor complex 2, a regulator of actin organization, is critical in these aspects of neuronal development

Muscular proprioception contributes to the control of interceptive actions.

International audienceThe authors proposed a model of the control of interceptive action over a ground plane (Chardenon, Montagne, Laurent, & Bootsma, 2004). This model is based on the cancellation of the rate of change of the angle between the current position of the target and the direction of displacement (i.e., the bearing angle). While several sources of visual information specify this angle, the contribution of proprioceptive information has not been directly tested. In this study, the authors used a virtual reality setup to study the role of proprioception when intercepting a moving target. In a series of experiments, the authors manipulated proprioceptive information by using the tendon vibration paradigm. The results revealed that proprioception is crucial not only to locate a moving target with respect to the body but also, and more importantly, to produce online displacement velocity changes to intercept a moving target. These findings emphasize the importance of proprioception in the control of interceptive action and illustrate the relevance of our model to account for the regulations produced by the participants

MOrphologically-aware Jaccard-based ITerative Optimization (MOJITO) for Consensus Segmentation

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MOrphologically-aware Jaccard-based ITerative Optimization (MOJITO) for Consensus Segmentation

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Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)

International audienceThe extraction of consensus segmentations from several binary or probabilistic masks is important to solve various tasks such as the analysis of inter-rater variability or the fusion of several neural network outputs. One of the most widely used methods to obtain such a consensus segmentation is the STAPLE algorithm. In this paper, we first demonstrate that the output of that algorithm is heavily impacted by the background size of images and the choice of the prior. We then propose a new method to construct a binary or a probabilistic consensus segmentation based on the Fréchet means of carefully chosen distances which makes it totally independent of the image background size. We provide a heuristic approach to optimize this criterion such that a voxel’s class is fully determined by its voxel-wise distance to the different masks, the connected component it belongs to and the group of raters who segmented it. We compared extensively our method on several datasets with the STAPLE method and the naive segmentation averaging method, showing that it leads to binary consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than Mask Averaging and STAPLE methods. Our code is available at https://gitlab.inria.fr/dhamzaou/jaccardmap</a

Automatic Zonal Segmentation of the Prostate from 2D and 3D T2-weighted MRI and Evaluation for Clinical Use

International audiencePurpose: An accurate zonal segmentation of the prostate is required for prostate cancer management with MRI.Approach:The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial andchannel-wise attention mechanisms and uses loss functions to enforce prostate partition. The method was applied ona private multicentric 3D T2w MRI dataset and on the public 2D T2w MRI dataset ProstateX. To assess the modelperformance, the structures segmented by the algorithm on the private dataset were compared with those obtained byseven radiologists of various experience levels.Results: On the private dataset, we obtained a Dice score (DSC) of 93.90 ± 2.85 for the whole gland (WG), 91.00 ± 4.34 for the transition zone (TZ) and 79.08 ± 7.08 for the peripheral zone (PZ). Results were significantly better thanother compared networks’ (p-value<.05). On ProstateX we obtained a DSC of 90.90 ± 2.94 for WG, 86.84 ± 4.33 forTZ and 78.40 ± 7.31 for PZ. These results are similar to state-of-the art results and, on the private dataset, are coherent with those obtained by radiologists. Zonal locations and sectorial positions of lesions annotated by radiologists were also preserved.Conclusions: Deep learning-based methods can provide an accurate zonal segmentation of the prostate leading toa consistent zonal location and sectorial position of lesions, and therefore can be used as a helping tool for prostatecancer diagnosis

Segmentation automatique de la prostate à l’aide d’un réseau de neurones profond

National audienceObjectifsL’objectif de l’étude est le développement d’un outil de segmentation automatique de l’anatomie zonale prostatique sur la séquence en pondération T2 en IRM, basé sur l’apprentissage profond (réseau de neurones), robuste quelle que soit la variabilité morphologique de la prostate et les caractéristiques techniques des séquences (constructeurs, aimants, épaisseur de coupes, champs de vue).MéthodesNous avons utilisé une variante du réseau U-Net 3D de Isensee avec le Dice généralisé pour fonction de coût, que nous avons entraîné sur les bases de données publiques ProstateX (79 volumes) et PROMISE12 (50 volumes) ré-échantillonées à une résolution de 1 × 1 × 3 mm, avec une répartition entraînement/validation de 4/1, et ce durant 150 epochs. En post-traitement nous avons extrait la plus grande zone connexe.Pour les tests nous avons utilisé les bases de données NCI-ISBI-Dx (29 volumes) et MSD-P (32 volumes), ainsi qu’une base de donnée privée (10 volumes, 2 types de constructeurs).RésultatsPour évaluer la performance du réseau nous avons utilisé une mesure volumique (Dice). Les Résultats étaient variables, fonction des bases de données utilisées :– sur la base de données MSD-P, dont les caractéristiques images sont très homogènes (même centre et même constructeur machine) le Dice moyen était bon évalué à 0,899 (± 0,033) ;– sur la base de données NCI-ISBI-Dx, comportant des IRM acquises sur des machines de constructeurs différents, la performance est moindre mais correct avec un Dice moyen de 0,840 (± 0,028) ;– sur la base de données privée avec des constructeurs et des types de séquences différentes le Dice moyen était de 0,903 (± 0,019) à 0,610 (± 0,310). La performance était supérieure pour les données proches de celles utilisés à l’entraînement (Fig. 1, Fig. 2, Fig. 3).ConclusionCe réseau de neurones est la première étape d’un framework qui fonctionnera secondairement à des résolutions plus élevées sur une zone réduite, pour lequel il serait intéressant d’intégrer l’anisotropie et une métrique de distance spatiale. La performance du réseau est fortement liée aux caractéristiques des données utilisées pour l’entraînement (type de constructeur, champ de vue utilisé)