Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data

Durrleman, Stanley; Fletcher, Tom; Gerig, Guido; Niethammer, Marc

doi:10.1007/978-3-642-33555-6

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of MRI studies have detected atrophy in the entorhinal cortex (ERC), hippocampus and amygdala associated with clinical disease severity (Devanand et al, 2007;La Joie et al, 2012;Miller et al, 2015b) and years to AD dementia conversion (Atiya et al, 2003;Kantarci and Jack, 2003). More recent MRI studies have focused on evidence of atrophy that precede clinical symptoms (Jack et al, 2004;Csernansky et al, 2005;den Heijer et al, 2006;Apostolova et al, 2010;Dickerson et al, 2011;Miller et al, 2013;Soldan et al, 2015;Pettigrew et al, 2016), often detecting these smaller changes using time-series data analysis (Durrleman et al, 2012) and survival analysis. These MRI biomarkers of AD-related atrophy prior to manifestation of clinical symptoms are of interest because (1) they may aid in assessing efficacy of therapeutic interventions and (2) they may aid in the identification of populations that can benefit from therapeutic intervention prior to clinical symptoms.…”

Section: Introductionmentioning

confidence: 99%

Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

Kulason

Tward

et al. 2020

Front. Neurosci.

View full text Add to dashboard Cite

This study examines the atrophy patterns in the entorhinal and transentorhinal cortices of subjects that converted from normal cognition to mild cognitive impairment. The regions were manually segmented from 3T MRI, then corrected for variability in boundary definition over time using an automated approach called longitudinal diffeomorphometry. Cortical thickness was calculated by deforming the gray matter-white matter boundary surface to the pial surface using an approach called normal geodesic flow. The surface was parcellated based on four atlases using large deformation diffeomorphic metric mapping. Average cortical thickness was calculated for (1) manually-defined entorhinal cortex, and (2) manually-defined transentorhinal cortex. Group-wise difference analysis was applied to determine where atrophy occurred, and change point analysis was applied to determine when atrophy started to occur. The results showed that by the time a diagnosis of mild cognitive impairment is made, the transentorhinal cortex and entorhinal cortex was up to 0.6 mm thinner than a control with normal cognition. A change point in atrophy rate was detected in the transentorhinal cortex 9–14 years prior to a diagnosis of mild cognitive impairment, and in the entorhinal cortex 8–11 years prior. The findings are consistent with autopsy findings that demonstrate neuronal changes in the transentorhinal cortex before the entorhinal cortex.

show abstract

Section: Introductionmentioning

confidence: 99%

Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

Kulason

Tward

et al. 2020

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The 3D atlases for each of the groups (normal, CoA, RAA+ALSA, DAA) were created using the classical MIRTK atlas generation tool [8, 9] (Fig. 2).…”

Section: Methodsmentioning

confidence: 99%

3D MRI atlases of congenital aortic arch anomalies and normal fetal heart: application to automated multi-label segmentation

Uus

Poppel

Steinweg

et al. 2022

Preprint

View full text Add to dashboard Cite

The black blood contrast T2w ssFSE fetal MRI along with application of 3D slice-to-volume registration methods allows reconstruction of high-resolution 3D images of the heart that provide superior visualisation of fetal cardiovascular anomalies. However, there is a lack of formalisation of the MRI appearance of fetal cardiovascular anatomy and standardisation of vessel segmentation protocols. In this work, we present the first 3D fetal MRI atlases defining normal and abnormal fetal cardiovascular anatomy. We also perform evaluation of the feasibility of atlas-guided registration and deep learning methods for automated 3D multi-label vessel segmentation.

show abstract

3D black blood cardiovascular magnetic resonance atlases of congenital aortic arch anomalies and the normal fetal heart: application to automated multi-label segmentation

Uus

Poppel

Steinweg

et al. 2022

Journal of Cardiovascular Magnetic Resonance

View full text Add to dashboard Cite

Background Image-domain motion correction of black-blood contrast T2-weighted fetal cardiovascular magnetic resonance imaging (CMR) using slice-to-volume registration (SVR) provides high-resolution three-dimensional (3D) images of the fetal heart providing excellent 3D visualisation of vascular anomalies [1]. However, 3D segmentation of these datasets, important for both clinical reporting and the application of advanced analysis techniques is currently a time-consuming process requiring manual input with potential for inter-user variability. Methods In this work, we present novel 3D fetal CMR population-averaged atlases of normal and abnormal fetal cardiovascular anatomy. The atlases are created using motion-corrected 3D reconstructed volumes of 86 third trimester fetuses (gestational age range 29-34 weeks) including: 28 healthy controls, 20 cases with postnatally confirmed neonatal coarctation of the aorta (CoA) and 38 vascular rings (21 right aortic arch (RAA), 17 double aortic arch (DAA)). We used only high image quality datasets with isolated anomalies and without any other deviations in the cardiovascular anatomy.In addition, we implemented and evaluated atlas-guided registration and deep learning (UNETR) methods for automated 3D multi-label segmentation of fetal cardiac vessels. We used images from CoA, RAA and DAA cohorts including: 42 cases for training (14 from each cohort), 3 for validation and 6 for testing. In addition, the potential limitations of the network were investigated on unseen datasets including 3 early gestational age (22 weeks) and 3 low SNR cases. Results We created four atlases representing the average anatomy of the normal fetal heart, postnatally confirmed neonatal CoA, RAA and DAA. Visual inspection was undertaken to verify expected anatomy per subgroup. The results of the multi-label cardiac vessel UNETR segmentation showed 100$$\%$$ % per-vessel detection rate for both normal and abnormal aortic arch anatomy. Conclusions This work introduces the first set of 3D black-blood T2-weighted CMR atlases of normal and abnormal fetal cardiovascular anatomy including detailed segmentation of the major cardiovascular structures. Additionally, we demonstrated the general feasibility of using deep learning for multi-label vessel segmentation of 3D fetal CMR images.

show abstract

Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data

Cited by 3 publications

References 15 publications

Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

3D MRI atlases of congenital aortic arch anomalies and normal fetal heart: application to automated multi-label segmentation

3D black blood cardiovascular magnetic resonance atlases of congenital aortic arch anomalies and the normal fetal heart: application to automated multi-label segmentation

Contact Info

Product

Resources

About