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

Promoting the use of the PRECISE score for prostate MRI during active surveillance: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

OBJECTIVES: The PRECISE criteria for serial multiparametric magnetic resonance imaging (MRI) of the prostate during active surveillance recommend the use of a dedicated scoring system (PRECISE score) to assess the likelihood of clinically significant radiological change. This pilot study assesses the effect of an interactive teaching course on prostate MRI during active surveillance in assessing radiological change in serial imaging. METHODS: Eleven radiology fellows and registrars with different experience in prostate MRI reading participated in a dedicated teaching course where they initially evaluated radiological change (based on their previous training in prostate MRI reading) independently in fifteen patients on active surveillance (baseline and follow-up scan), and then attended a lecture on the PRECISE score. The initial scans were reviewed for teaching purposes and afterwards the participants re-assessed the degree of radiological change in a new set of images (from fifteen different patients) applying the PRECISE score. Receiver operating characteristic analysis was performed. Confirmatory biopsies and PRECISE scores given in consensus by two radiologists (involved in the original draft of the PRECISE score) were the reference standard. RESULTS: There was a significant improvement in the average area under the curve (AUC) for the assessment of radiological change from baseline (AUC: 0.60 [Confidence Intervals: 0.51-0.69] to post-teaching (AUC: 0.77 [0.70-0.84]). This was an improvement of 0.17 [0.016-0.28] (p = 0.004). CONCLUSIONS: A dedicated teaching course on the use of the PRECISE score improves the accuracy in the assessment of radiological change in serial MRI of the prostate

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)

Kidney stones and imaging: what can your radiologist do for you?

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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

Diagnosis of prostate cancer in one day: The benefits of cytology in tumour detection

International audienceObjectiveProstate cancer (PCa) is a frequent and mortal disease. The aim of this study was to introduce a “diagnosis and handling of PCa in one day” concept, accelerating the handling of PCa patients by giving diagnostic results within 3 hours with the help of prostate cytology. Standard histology served as the control.Material and methodsAfter multiparametric MRI, prostate biopsies were taken and one was used for imprint cytology on superfrost slides. The cytology samples were stained by p63/p504s double staining, a standard stain in PCa histology, followed by on‐site interpretation.ResultsAmong 129 patients, 39.5% had a prior history of PCa and were either under active surveillance or had been treated by focal therapy. The others came with suspicion of PCa. In 80.8% of the cases, the cytology and histology results agreed. In low‐grade PCa the detection with cytology was more difficult with 72.4% agreement, whereas for intermediate and high‐grade PCa the concordance with histology was 81.8 and 90%, respectively. False positive cases were less than 4.0%.ConclusionCytology of the prostate is unusual, but our study is the first to show it is feasible and gives immediate results that are satisfactory, especially in more aggressive cases. Immunocytology can be easily integrated into the laboratory. Our technique allows quicker handling of PCa, which can soften the psychological impact on men waiting for the diagnosis of PCa

3.0 T prostate MRI: Visual assessment of 2D and 3D T2-weighted imaging sequences using PI-QUAL score

International audiencePurpose: Comparing 2D and 3D T2 weighted sequences in terms of image quality in 3.0 T MRI with readers of varied experiences, using PI-QUAL inspired criteria.Methods: 91 male patients with suspected prostate cancer (PCa) underwent diagnostic prostate MRI on a 3.0 T MR system using a 32-channel phased-array torso coil before prostate biopsy. MRI protocol included 3D T2w images, axial 2D T2w images, axial diffusion-weighted images (DWI) with the corresponding ADC apparent diffusion coefficient maps, and axial dynamic contrast enhanced images. 3D T2w and 2D T2w imaging were compared by 4 radiologists using a Likert scale for image quality (overall anatomy, delineation of capsule, seminal vesicles, ejaculatory ducts, sphincter muscle, artifacts), tumor delimitation and conspicuity.Results: No significant differences in terms of overall quality between 3D and 2D T2w images were found. However 2D T2w demonstrated higher rating than 3D T2w images as for the image quality of the external capsule, sphincter muscle and ejaculatory ducts delineation (p < 0.05).Conclusion: 3D T2w sequence can’t replace 2D T2w sequence, despite good quality images but it remains more prone to artifacts. Quality of 2D T2w sequences was substantially superior to 3D sequences for delineation of key structures as external capsule, sphincter muscle. The use of PI-QUAL criteria allows reproducible analysis of the quality of T2 weighted images

Mp67-05 first round of trans-rectal targeted biopsies with magnetic resonance imaging/ultrasound-fusion images versus conventional ultrasound- guided biopsies for the diagnosis of localised prostate cancer.

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