CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans

Unsupervised pathology detection can be implemented by training a model on healthy data only and measuring the deviation from the training set upon inference, for example with CNN-based feature extraction and one-class classifiers, or reconstruction-score-based methods such as AEs, GANs and Diffusion models. Normalizing Flows (NF) have the ability to directly learn the probability distribution of training examples through an invertible architecture. We leverage this property in a novel 3D NF-based model named CT-3DFlow, specifically tailored for patient-level pulmonary pathology detection in chest CT data. Our model is trained unsupervised on healthy 3D pulmonary CT patches, and detects deviations from its log-likelihood distribution as anomalies. We aggregate patches-level likelihood values from a patient's CT scan to provide a patient-level 'normal'/'abnormal' prediction. Out-of-distribution detection performance is evaluated using expert annotations on a separate chest CT test dataset, outperforming other state-of-the-art methods

Modèles structurels flous et propagation de contraintes pour la segmentation et la reconnaissance d'objets dans les images: Application aux structures normales et pathologiques du cerveau en IRM

Le cerveau présente une structure complexe. La segmentation et la reconnaissance automatique de ses sous-structures dans des IRM cérébrales est délicate et nécessite donc l'utilisation d'un modèle de l'anatomie. L'utilisation d'atlas iconiques est efficace pour traiter les données de sujets sains mais son adaptation au traitement de cas pathologiques reste problématique. Dans cette thèse nous utilisons un modèle symbolique de l'anatomie proche des descriptions linguistiques qui comprend les principales structures cérébrales. L'agencement spatial de ces structures y est représenté sous forme de relations spatiales et leur apparence est caractérisée par des relations sur leur contraste. Réaliser la reconnaissance grâce à ce modèle structurel consiste à obtenir pour chaque structure une région de l'image vérifiant les relations et caractéristiques portées par le modèle. Nous formulons ce problème comme un réseau de contraintes dont les variables sont les régions recherchées représentées sous forme d'ensembles flous. Les contraintes sont déduites du modèle en tirant parti de modélisations floues. Une contribution nouvelle porte sur la contrainte de connexité et la proposition de définitions et algorithmes adaptés au cas flou présentant de bonnes propriétés. Nous mettons alors en œuvre un algorithme de propagation de contraintes qui itérativement réduit l'espace de solutions. Enfin nous obtenons un résultat pour certaines structures d'intérêt par l'extraction d'une surface minimale relativement aux résultats de l'algorithme de propagation. Nous appliquons cette approche aux structures internes du cerveau chez des sujets sains. Finalement nous étendons ce processus au traitement de données de patients présentant une tumeur. Le modèle générique ne correspondant plus aux données à reconnaître, nous proposons un algorithme de propagation recherchant à la fois le modèle spécifique au patient et les structures anatomiques

Modèles structurels flous et propagation de contraintes pour la segmentation et la reconnaissance d'objets dans les images (application aux structures normales et pathologiques du cerveau en IRM)

Le cerveau présente une structure complexe. La segmentation et la reconnaissance automatique de ses sous-structures dans des IRM cérébrales est délicate et nécessite donc l'utilisation d'un modèle de l'anatomie. L'utilisation d'atlas iconiques est efficace pour traiter les données de sujets sains mais son adaptation au traitement de cas pathologiques reste problématique. Dans cette thèse nous utilisons un modèle symbolique de l'anatomie proche des descriptions linguistiques qui comprend les principales structures cérébrales. L'agencement spatial de ces structures y est représenté sous forme de relations spatiales et leur apparence est caractérisée par des relations sur leur contraste. Réaliser la reconnaissance grâce à ce modèle structurel consiste à obtenir pour chaque structure une région de l'image vérifiant les relations et caractéristiques portées par le modèle. Nous formulons ce problème comme un réseau de contraintes dont les variables sont les régions recherchées représentées sous forme d'ensembles flous. Les contraintes sont déduites du modèle en tirant parti de modélisations floues. Une contribution nouvelle porte sur la contrainte de connexité et la proposition de définitions et algorithmes adaptés au cas flou présentant de bonnes propriétés. Nous mettons alors en œuvre un algorithme de propagation de contraintes qui itérativement réduit l'espace de solutions. Enfin nous obtenons un résultat pour certaines structures d'intérêt par l'extraction d'une surface minimale relativement aux résultats de l'algorithme de propagation. Nous appliquons cette approche aux structures internes du cerveau chez des sujets sains. Finalement nous étendons ce processus au traitement de données de patients présentant une tumeur. Le modèle générique ne correspondant plus aux données à reconnaître, nous proposons un algorithme de propagation recherchant à la fois le modèle spécifique au patient et les structures anatomiques.The anatomy of brain is complex. Therefore the fully automatic segmentation and recognition of its relevant subparts in brain MRI is a challenging task. It is usually done using a model of anatomy. In this thesis we use a symbolic model of anatomy, close to linguistic descriptions. It includes the main brain structures and some of their properties.Their spatial layout is encoded as spatial relations and their appearance is represented as relations on their contrast. We use this structural model to perform the recognition : we have to obtain for each anatomical structure a region of the image that fulfils all relations and characteristics of the model. We formulate this problem as a constraint network whose variables are the sought regions represented as fuzzy sets. The constraints are derived from the model using fuzzy modeling. In particular to obtain the connectivity constraint, we propose a new definition (and the associated algorithms) for the connectivity of fuzzy sets.Then we implement a constraint propagation algorithm which iteratively reduces the solution space. Once the solution space has been reduced, we obtain a final result for some structures. We extract a minimal surface with respect to the outputs of the propagation algorithm. We apply this approach to brain internal structures of healthy subjects. Finally we propose an extension to handle cases that present brain tumors. The generic model of anatomy does not fit anymore the data to be recognized. Therefore we propose a propagation algorithm that searches simultaneously for the specific model of the patient for the anatomical structures.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Propagation de contraintes pour la segmentation et la reconnaissance de structures anatomiques à partir d'un modèle structurel

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A constraint propagation approach to structural model based image segmentation and recognition

International audienceThe interpretation of complex scenes in images requires knowledge regarding the objects in the scene and their spatial arrangement. We propose a method for simultaneously segmenting and recognizing objects in images, that is based on a structural representation of the scene and a constraint propagation method. The structural model is a graph representing the objects in the scene, their appearance and their spatial relations, represented by fuzzy models. The proposed solver is a novel global method that assigns spatial regions to the objects according to the relations in the structural model. We propose to progressively reduce the solution domain by excluding assignments that are inconsistent with a constraint network derived from the structural model. The final segmentation of each object is then performed as a minimal surface extraction. The contributions of this paper are illustrated through the example of brain structure recognition in magnetic resonance images

A constraint propagation approach to structural model based image segmentation and recognition

International audienceThe interpretation of complex scenes in images requires knowledge regarding the objects in the scene and their spatial arrangement. We propose a method for simultaneously segmenting and recognizing objects in images, that is based on a structural representation of the scene and a constraint propagation method. The structural model is a graph representing the objects in the scene, their appearance and their spatial relations, represented by fuzzy models. The proposed solver is a novel global method that assigns spatial regions to the objects according to the relations in the structural model. We propose to progressively reduce the solution domain by excluding assignments that are inconsistent with a constraint network derived from the structural model. The final segmentation of each object is then performed as a minimal surface extraction. The contributions of this paper are illustrated through the example of brain structure recognition in magnetic resonance images

J Math Imaging Vis (2009) 34: 107–136 DOI 10.1007/s10851-009-0136-3 A New Fuzzy Connectivity Measure for Fuzzy Sets And Associated Fuzzy Attribute Openings

Abstract Fuzzy set theory constitutes a powerful representation framework that can lead to more robustness in problems such as image segmentation and recognition. This robustness results to some extent from the partial recovery of the continuity that is lost during digitization. In this paper we deal with connectivity measures on fuzzy sets. We show that usual fuzzy connectivity definitions have some drawbacks, and we propose a new definition that exhibits better properties, in particular in terms of continuity. This definition leads to a nested family of hyperconnections associated with a tolerance parameter. We show that corresponding connected components can be efficiently extracted using simple operations on a max-tree representation. Then we define attribute openings based on crisp or fuzzy criteria. We illustrate a potential use of these filters in a brain segmentation and recognition process. This work has been partly supported by a grant from the Nationa

Tailored 3D CT contrastive pretraining to improve pulmonary pathology classification

International audienceLearning useful representations is a key task for supervised, unsupervised, and self-supervised algorithms. These latter methods have shown great promise for learning meaningful visual representations from unlabeled data, which can then be readily used for downstream tasks (e.g., classification). Recently proposed contrastive self-supervised learning methods have shown high performance on natural images. In this work, we show the usefulness of such approaches in the medical domain when used for 3D chest CT patch pathology classification. We observe that pretraining on unlabeled domain-specific medical images using contrastive self-supervised learning with specific data transformation significantly improves the accuracy of our 3D patch-based pathology classifiers. Specifically, we show that contrastive pretraining outperforms end-to-end supervised training by a large margin (weighted accuracy: 90.33 % vs 77.75 %, respectively)

Cooperating Networks To Enforce A Similarity Constraint In Paired But Unregistered Multimodal Liver Segmentation

International audienceWe propose a method for segmenting two unregistered images from different modalities of the same patient and study at once, while enforcing a similarity constraint between the two segmentation masks. Our method relies on a segmentation network and a registration network, cooperating to get accurate and consistent segmentation masks across modalities, while forcing the segmentor to use all information available. Experiments on a dataset of T1 and T2-weighted liver MRI show that our method enables to get more similar segmentation masks across modalities than manual annotations, without deteriorating the performance (Dice =0.95 for T1, 0.92 for T2)