Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies

The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT\u27NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging

Dear reviewers: responses to common reviewer critiques about infant neuroimaging studies

The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT'NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging.R01 MH104324 - NIMH NIH HHS; UL1 TR001863 - NCATS NIH HHS; P50 MH115716 - NIMH NIH HHS; K01 MH108741 - NIMH NIH HHS; TL1 TR001864 - NCATS NIH HHS; R01 MH118285 - NIMH NIH HHS; U01 MH110274 - NIMH NIH HHS; P50 MH100029 - NIMH NIH HHS; ZIA MH002782 - Intramural NIH HHS; R01 EB027147 - NIBIB NIH HHS; R01 MH119251 - NIMH NIH HHS; UL1 TR003015 - NCATS NIH HHS; F31 HD102156 - NICHD NIH HHS; KL2 TR003016 - NCATS NIH HHS; T32 MH018268 - NIMH NIH HHSPublished versio

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

Spatio temporal modelling of dynamic developmental patterns

Spatio Temporal Modelling plays an important role in personalized medicine, virtual clinical trials or drug target identification. It enables the encoding of trajectories of complex diseases, metabolic or developmental pathways, to optimise an individual’s disease treatment or determine a developmental status. Dynamic Developmental Patterns (DDP) form the main challenge in modelling trajectories, constituted of the incompleteness and irregularity of observations, inter-patient variability and impairing factors like comorbidity, age or individual treatment response. The focus of this thesis lies in providing new strategies for the spatio- temporal modelling of dynamic developmental patterns, to encode and understand baseline trajectories disentangled from time-dependent or systemic dynamics. Thus, on the one hand the identification of suitable baseline states is essential and on the other hand the development of techniques to analyse the dynamics’ deviations and relations to the baseline. Here, it is demonstrated that the proposed modelling concept is capable to flexibly model DDPs independent of the imaging modalities, of different populations/age ranges and applications to answer research questions in the field of computer vision, cancer research, brain development and functional connectivity network analysis. It leads to the development of novel data representation forms for DDPs, segmentation strategies, classification procedures and time-dependent prediction approaches, outperforming state of the art methods.16

Longitudinal diffeomorphic fetal brain atlas learning for tissue labeling using geodesic regression and graph cuts

Zusammenfassung in deutscher SpracheDas Gehirn eines Fötus weist während des zweiten und dritten Schwangerschaftstrimesters Veränderungen sowohl in der Größe als auch Morphologie auf, welche auf Wachstumsprozesse des Hirnes und Faltungsprozesse der Hirnoberfläche zurück zu führen sind. Eine geeignete Aufnahmemodalität für das Hirn des Fötus ist die beschleunigte Magnetresonanztomographie. Sie ermöglicht es auf nicht invasivem Weg Bilder innerhalb von 20 Sekunden aufzunehmen, um Bewegungsartefakten, erzeugt durch die fetale Bewegung, entgegen zu wirken. Diese Technik weist jedoch ein Problem der Konstanz von Grauwerten für gleiche Strukturen auf. Um fetale Hirne vergleichen zu können wird ein Atlas als ein Referenzmodell verwendet, welcher daraufhin die Untersuchung der Gehirnentwicklung, fetaler Pathologielokalisationen, von Abnormalitäten des Fötus oder dessen Hirnanatomie ermöglicht. Bei der Erstellung eines Atlas für das Hirn eines Fötus müssen sowohl die strukturellen Veränderungen in Form und Größe als auch patientenbezogene Unterschiede des Hirnes einbezogen werden. Daher ist es Ziel dieser Masterarbeit ein kontinuierliches Modell der Hirnentwicklung zu erstellen, um dieses als Ausgang für die automatisierte Markierung von Hirnstrukturen zu verwenden, welches in einem selbsterstellten Framework integriert ist. Diese Arbeit stellt ein neues Konzept zur Berechnung eines spatio-temporalen fötalen Hirnatlas- unter der Verwendung von geodätischer Bildregression vor. Anhand der Analyseergebnisse des Atlasbildungsprozesses werden drei Altersgruppen definiert, um gezielt drei unterschiedliche Atlanten angepasst auf den Entwicklungsstatus des fetalen Hirns zu modellieren. Der für die Evaluierung verwendete Datensatz besteht aus 45 T2 gewichteten 1:5 Tesla Magnetresonanz-Bildern von Föten im Alter zwischen 18 und 30 Schwangerschaftswochen. Das vorgestellte Framework verwendet den berechneten Atlas als Kostenterm in einem Graph Cut Ansatz um automatisiert kortikale Hirnstrukturen und Ventrikel zu segmentieren. Vom Framework automatisiert bestimmte Segmentierungen für Kortexstrukturen weisen einen Dice Koeffizienten bis zu 0:85 und für Ventrikelstrukturen bis zu 0:60 auf.The human brain undergoes structural changes in size and in morphology between the second and the third trimester of pregnancy, according to accelerated growth and the progress of cortical folding. The most accurate non-invasive method for observing these events is the fast Magnetic Resonance (MR) imaging technique. It allows to image a fetus within 20 seconds and consequently reduce artefacts, caused by the fetal movement. A problem of MR imaging is the lack of comparability and constancy of gray-values. To make fetal brains comparable, atlases are used as a standard space for studying brain development, fetal pathology locations, fetal abnormalities or anatomy. The fetal atlas building process takes into account both inter-patient variability of brain shapes and the gestational age dependent structural changes. Thus, a time-varying atlas is required. The aim of the work is to provide a continuous model of brain development and to use it as base for an automatic tissue labeling framework. This master-s thesis provides a novel longitudinal fetal brain atlas construction concept for geodesic image regression using three different age-ranges which are parametrized according to the developmental stage of the fetus. The dataset used for evaluation contains 45 T2-weighted 1:5 Tesla MR images between Gestational Week (GW) 18:0 and GW 30 day 2. The proposed tissue labeling framework uses the learned spatio-temporal atlas as cost term in a graph cut based annotation procedure to automatically segment cortical and ventricle brain tissue. The automatic tissue labeling framework estimates cortical segmentations with a Dice Coefficient (DC) up to 0:85 and ventricle segmentations with a DC up to 0:60.12

Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data

Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework's ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.Comment: Preprint submitted to NeuroImag

Improving Aleatoric Uncertainty Quantification in Multi-annotated Medical Image Segmentation with Normalizing Flows

Quantifying uncertainty in medical image segmentation applications is essential, as it is often connected to vital decision-making. Compelling attempts have been made in quantifying the uncertainty in image segmentation architectures, e.g. to learn a density segmentation model conditioned on the input image. Typical work in this field restricts these learnt densities to be strictly Gaussian. In this paper, we propose to use a more flexible approach by introducing Normalizing Flows (NFs), which enables the learnt densities to be more complex and facilitate more accurate modeling for uncertainty. We prove this hypothesis by adopting the Probabilistic U-Net and augmenting the posterior density with an NF, allowing it to be more expressive. Our qualitative as well as quantitative (GED and IoU) evaluations on the multi-annotated and single-annotated LIDC-IDRI and Kvasir-SEG segmentation datasets, respectively, show a clear improvement. This is mostly apparent in the quantification of aleatoric uncertainty and the increased predictive performance of up to 14%. This result strongly indicates that a more flexible density model should be seriously considered in architectures that attempt to capture segmentation ambiguity through density modeling. The benefit of this improved modeling will increase human confidence in annotation and segmentation, and enable eager adoption of the technology in practice