Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction

Tuning the regularization hyperparameter $\alpha$ in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of $\alpha$ to a given setting of interest in a quantitative manner. In this work, we propose a simulation-based approach to tune $\alpha$ for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal $\alpha$, chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting.Comment: 11 pages. This work has been submitted to MICCAI 202

Segmentation of the cortical plate in fetal brain MRI with a topological loss

The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging. An accurate MR image segmentation, and more importantly a topologically correct delineation of the cortical gray matter, is a key baseline to perform further quantitative analysis of brain development. In this paper, we propose for the first time the integration of a topological constraint, as an additional loss function, to enhance the morphological consistency of a deep learning-based segmentation of the fetal cortical plate. We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21 to 38 weeks of gestation, showing the significant benefits of our method through all gestational ages as compared to a baseline method. Furthermore, qualitative evaluation by three different experts on 130 randomly selected slices from 26 clinical MRIs evidences the out-performance of our method independently of the MR reconstruction quality.Comment: 4 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Volumetric reconstruction and determination of minimum crosssectional area of the pharynx in patients with cleft lip and palate: comparison between two different softwares

Objective: The aim of this study was to assess the accuracy of volumetric reconstruction of the pharynx by comparing the volume and minimum crosssectional area (mCSA) determined with open-source applications (ITK-Snap, www.itksnap.org ; SlicerCMF) and commercial software (Dolphin3D, 11.8, Dolphin Imaging & Management Solutions, Chatsworth, CA, USA) previously validated in the literature. Material and Methods: The sample comprised of 35 cone-beam computed tomography (CBCT) scans of patients with unilateral cleft lip and palate, with mean age of 29±15. Three-dimensional volumetric models of the pharynx were reconstructed using semi-automatic segmentation using the applications ITK-Snap (G1) and Dolphin3D (G2). Volumes and minimum cross-sectional areas were determined. Inter- and intra-observer error were calculated using ICC test. Comparison between applications was calculated using the Wilcoxon test. Results: Volumes and minimum crosssectional area were statistically similar between applications. ITK-Snap showed higher pharynx volumes, but lower mCSA. Visual assessment showed that 62.86% matched the region of mCSA in Dolphin3D and SPHARM-PDM. Conclusion:Measurements of volume and mCSA are statistically similar between applications. Therefore, open-source applications may be a viable option to assess upper airway dimensions using CBCT exams

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Development and validation of robust MR image reconstruction and segmentation techniques for the quantitative analysis of the fetal brain

Formation and development of the human brain is initiated in utero and carries on until young adulthood. During the prenatal period, most significant morphological changes occur, following well-defined spatiotemporal patterns. Eventual disruption occurring during these periods of vulnerability may have major impact later in life. It is, therefore, of the utmost importance to get a better understanding of the fetal brain development. Magnetic resonance imaging (MRI) is a non-invasive technique that relies on the tissue properties of to generate the image intensities. Towards the quantitative analy- sis, the fetal brain MRI workflow gathers the image acquisition, the image resolution enhancement through super-resolution (SR) reconstruction and the reduction of image complexity with the tissue segmentation. This thesis focuses on the development and validation of robust automated tools for the quantitative analysis of the fetal brain in MRI. Specifically, we address two key steps that encounter fetal-specific challenges: the SR reconstruction and the tissue segmentation. In practice, both are hindered by the major obstacle of data scarcity of fetal brain MRI. With the impossibility to acquire motion-free high resolution images, the validation of SR reconstruction is troublesome. Our first contribution is a multi-observer multi-dataset study to validate the practical value of SR reconstruction in a clinical environment. We evidence that SR does not introduce spatial distortions and increases the confidence of the observer. Furthermore, we propose a simulation-based approach for the enhancement of the overall SR-reconstructed image intensity contrast. Automatic tissue segmentation methods must generalize to be robust to the many sources of variations that may be induced by the gestation-long maturation, the acquisition system or the SR reconstruction method. We propose novel data augmentation strategies in order to increase the heterogeneity of the data. Our methods, either relying on a simulation framework or a multi-reconstruction approach, increases the generalizability of deep-learning (DL) based segmentation models. Finally, a major methodological contribution of this thesis is the topologically-constrained DL frame- work for the cortical plate segmentation. Overall, our contributions in image reconstruction and tissue segmentation take a step forward in the accuracy, generalizability and translation of methods. Although some limitations remain, the combination of these advanced engineering methods set solid grounds for the study of the in utero brain development. -- La formation et le développement du cerveau humain commencent in utero et se pour- suivent jusqu’au début de l’âge adulte. Au cours de la période prénatale les change- ments morphologiques les plus importants se produisent selon des schémas spatio- temporels bien définis. Les éventuelles perturbations survenant au cours de ces péri- odes de vulnérabilité peuvent avoir un impact majeur plus tard dans la vie. Il est donc important de mieux comprendre le développement du cerveau fœtal. L’imagerie par résonance magnétique (IRM) est une technique non invasive qui s’appuie sur les propriétés des tissus pour générer des variations d’intensité, et construire une image. Les différentes étapes de l’analyse quantitative du cerveau fœtal dérivé de l’IRM comprennent l’acquisition de l’image, l’amélioration de la résolution de l’image par la reconstruction en super-résolution (SR) et la réduction de la complex- ité de l’image par la segmentation des tissus. Cette thèse se concentre sur le dévelop- pement et la validation d’outils automatisés robustes pour l’analyse quantitative du cerveau fœtal en IRM. Plus précisément, nous abordons deux étapes clés qui rencon- trent actuellement des difficultés spécifiques à l’imagerie fœtale : la reconstruction en SR et la segmentation des tissus. En pratique, ces deux étapes sont freinées et éprou- vées par la rareté des données IRM du cerveau fœtal. Avec l’impossibilité d’acquérir des images haute résolution sans mouvement, la val- idation de la reconstruction SR est difficile. Notre première contribution est une étude multi-observateurs et multi-dataset pour valider la valeur pratique de la reconstruction SR dans un environnement clinique. Nous démontrons que la SR n’introduit pas de distorsions anatomiques et augmente l’assurance de l’observateur. En outre, nous pro- posons une approche basée sur la simulation pour l’amélioration du contraste global d’intensité de l’image reconstruite par SR. Les méthodes de segmentation automatique des tissus doivent, pour être robustes, être généralisées aux nombreuses sources de variations qui peuvent être induites par la maturation pendant la gestation, le système d’acquisition ou la méthode de reconstruction SR. Nous proposons de nouvelles stratégies d’augmentation de données afin d’en accroître l’hétérogénéité. Nos méthodes, qui s’appuient soit sur la simulation d’images de synthèse, soit sur une approche de multi-reconstruction, augmentent la généralisation des modèles de segmentation par apprentissage automatique. Finale- ment, la contribution méthodologique majeure de cette thèse est l’intégration d’une contrainte topologique dans l’entrainement de méthode de segmentation par apprentissage automatique pour la plaque corticale. Dans l’ensemble, ce travail permet une avancée majeure en terme de précision, généralisation et implémentation des méthodes quant à la reconstruction et à la segmentation des tissus en IRM. Bien que certaines limites subsistent, la combinaison de ces méthodes d’ingénierie avancées constitue une base solide pour l’étude du développement du cerveau in utero

Synthetic Magnetic Resonance Images for Domain Adaptation: Application to Fetal Brain Tissue Segmentation

The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust segmentation. In this context, we use FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain along with its class labels. We demonstrate that these multiple synthetic annotated data, generated at no cost and further reconstructed using the target super-resolution technique, can be successfully used for domain adaptation of a deep learning method that segments seven brain tissues. Overall, the accuracy of the segmentation is significantly enhanced, especially in the cortical gray matter, the white matter, the cerebellum, the deep gray matter and the brainstem

Synthetic Magnetic Resonance Images for Domain Adaptation: Application to Fetal Brain Tissue Segmentation

The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust segmentation. In this context, we use FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain along with its class labels. We demonstrate that these multiple synthetic annotated data, generated at no cost and further reconstructed using the target super-resolution technique, can be successfully used for domain adaptation of a deep learning method that segments seven brain tissues. Overall, the accuracy of the segmentation is significantly enhanced, especially in the cortical gray matter, the white matter, the cerebellum, the deep gray matter and the brainstem

Volumetric reconstruction and determination of minimum crosssectional area of the pharynx in patients with cleft lip and palate: comparison between two different softwares

Abstract Objective: The aim of this study was to assess the accuracy of volumetric reconstruction of the pharynx by comparing the volume and minimum crosssectional area (mCSA) determined with open-source applications (ITK-Snap, www.itksnap.org ; SlicerCMF) and commercial software (Dolphin3D, 11.8, Dolphin Imaging & Management Solutions, Chatsworth, CA, USA) previously validated in the literature. Material and Methods: The sample comprised of 35 cone-beam computed tomography (CBCT) scans of patients with unilateral cleft lip and palate, with mean age of 29±15. Three-dimensional volumetric models of the pharynx were reconstructed using semi-automatic segmentation using the applications ITK-Snap (G1) and Dolphin3D (G2). Volumes and minimum cross-sectional areas were determined. Inter- and intra-observer error were calculated using ICC test. Comparison between applications was calculated using the Wilcoxon test. Results: Volumes and minimum crosssectional area were statistically similar between applications. ITK-Snap showed higher pharynx volumes, but lower mCSA. Visual assessment showed that 62.86% matched the region of mCSA in Dolphin3D and SPHARM-PDM. Conclusion: Measurements of volume and mCSA are statistically similar between applications. Therefore, open-source applications may be a viable option to assess upper airway dimensions using CBCT exams