Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

Krebs, Julian; Delingette, Hervé; Ayache, Nicholas; Mansi, Tommaso

doi:10.1109/tmi.2021.3056531

Cited by 15 publications

(9 citation statements)

References 34 publications

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“…We may even use decoders that are specifically designed for data, such as diffeomorphometry for brain grey matter. We detail the latter case, directly in the spirit of classical models with stationary velocity fields [7,14]. An additional parameter, a template T , is learned at the centered reference disease stage z ψ = 0 .…”

Section: Modularitymentioning

confidence: 99%

Longitudinal Self-supervision to Disentangle Inter-patient Variability from Disease Progression

Couronné¹,

Vernhet²,

Durrleman³

2021

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

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The problem of building disease progression models with longitudinal data has long been addressed with parametric mixed-effect models. They provide interpretable models at the cost of modeling assumptions on the progression profiles and their variability across subjects. Their deep learning counterparts, on the other hand, strive on flexible data-driven modeling, and additional interpretability -or, as far as generative models are involved, disentanglement of latent variables with respect to generative factors -comes from additional constraints. In this work, we propose a deep longitudinal model designed to disentangle inter-patient variability from an estimated disease progression timeline. We do not seek for an explicit mapping between age and disease stage, but to learn the latter solely from the ordering between visits using a differentiable ranking loss. Furthermore, we encourage inter-patient variability to be encoded in a separate latent space, where for each patient a single representation is learned from its set of visits, with a constraint of invariance under permutation of the visits. The modularity of the network architecture allows us to apply our model on various data types: a synthetic image dataset with known generative factors, cognitive assessments and neuroimaging data. We show that, combined with our patient encoder, the ranking loss for visits helps to exceed models with supervision, in particular in terms of disease staging.

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Section: Modularitymentioning

confidence: 99%

Longitudinal Self-supervision to Disentangle Inter-patient Variability from Disease Progression

Couronné¹,

Vernhet²,

Durrleman³

2021

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

View full text Add to dashboard Cite

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“…For cardiac images, Biffi et al [25] presented LVAE for interpretable classification of anatomical shapes into different clinical conditions. Krebs et al [26] proposed to learn a probabilistic motion model for spatio-temporal cardiac image registration. Reynaud et al [27] proposed a causal generative model to generate synthetic 3D ultrasound videos conditioned on a given input image and an expected ejection fraction.…”

Section: Introductionmentioning

confidence: 99%

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

Wang¹,

Qiu²,

Marvao³

et al. 2023

Preprint

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Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and to answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves a high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data.

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“…Therefore, researchers have introduced deep learning based methods into the cardiac MR image processing, which can provide fast computation once the model has finished the training. For example, researchers have used a convolutional neural network (CNN) for registration and segmentation of cardiac cine or late gadolinium enhancement images [18][19][20][21][22][23][24][25] and for T 1 /T 2 parameter fitting. 26,27 Most of the methods adopt a supervised learning strategy, which requires the ground truth for training.…”

Section: Introductionmentioning

confidence: 99%

Motion correction for native myocardial T₁ mapping using self‐supervised deep learning registration with contrast separation

Chunyan

et al. 2022

NMR in Biomedicine

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In myocardial T 1 mapping, undesirable motion poses significant challenges because uncorrected motion can affect T 1 estimation accuracy and cause incorrect diagnosis.In this study, we propose and evaluate a motion correction method for myocardial T 1 mapping using self-supervised deep learning based registration with contrast separation (SDRAP). A sparse coding based method was first proposed to separate the contrast component from T 1 -weighted (T1w) images. Then, a self-supervised deep neural network with cross-correlation (SDRAP-CC) or mutual information as the registration similarity measurement was developed to register contrast separated images, after which signal fitting was performed on the motion corrected T1w images to generate motion corrected T 1 maps. The registration network was trained and tested in 80 healthy volunteers with images acquired using the modified Look-Locker inversion recovery (MOLLI) sequence. The proposed SDRAP was compared with the free form deformation (FFD) registration method regarding (1) Dice similarity coefficient (DSC) and mean boundary error (MBE) of myocardium contours, (2) T 1 value and standard deviation (SD) of T 1 fitting, (3) subjective evaluation score for overall image quality and motion artifact level, and (4) computation time. Results showed that SDRAP-CC achieved the highest DSC of 85.0 ± 3.9% and the lowest MBE of 0.92 ± 0.25 mm among the methods compared. Additionally, SDRAP-CC performed the best by resulting in lower SD value (28.1 ± 17.6 ms) and higher subjective image quality scores (3.30 ± 0.79 for overall quality and 3.53 ± 0.68 for motion artifact) evaluated by a cardiologist. The proposed SDRAP took only 0.52 s to register one slice of MOLLI images, achieving about sevenfold acceleration over FFD (3.7 s/slice).

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Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

Cited by 15 publications

References 34 publications

Longitudinal Self-supervision to Disentangle Inter-patient Variability from Disease Progression

Longitudinal Self-supervision to Disentangle Inter-patient Variability from Disease Progression

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

Motion correction for native myocardial T₁ mapping using self‐supervised deep learning registration with contrast separation

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Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

Cited by 15 publications

References 34 publications

Longitudinal Self-supervision to Disentangle Inter-patient Variability from Disease Progression

Longitudinal Self-supervision to Disentangle Inter-patient Variability from Disease Progression

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

Motion correction for native myocardial T1 mapping using self‐supervised deep learning registration with contrast separation

Contact Info

Product

Resources

About

Motion correction for native myocardial T₁ mapping using self‐supervised deep learning registration with contrast separation