Incompressible image registration using divergence-conforming B-splines

Anatomically plausible image registration often requires volumetric preservation. Previous approaches to incompressible image registration have exploited relaxed constraints, ad hoc optimisation methods or practically intractable computational schemes. Divergence-free velocity fields have been used to achieve incompressibility in the continuous domain, although, after discretisation, no guarantees have been provided. In this paper, we introduce stationary velocity fields (SVFs) parameterised by divergence-conforming B-splines in the context of image registration. We demonstrate that sparse linear constraints on the parameters of such divergence-conforming B-Splines SVFs lead to being exactly divergence-free at any point of the continuous spatial domain. In contrast to previous approaches, our framework can easily take advantage of modern solvers for constrained optimisation, symmetric registration approaches, arbitrary image similarity and additional regularisation terms. We study the numerical incompressibility error for the transformation in the case of an Euler integration, which gives theoretical insights on the improved accuracy error over previous methods. We evaluate the proposed framework using synthetically deformed multimodal brain images, and the STACOM11 myocardial tracking challenge. Accuracy measurements demonstrate that our method compares favourably with state-of-the-art methods whilst achieving volume preservation.Comment: Accepted at MICCAI 201

Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations and to implicitly capture their underlying properties via reconstructing biomechanical simulations. The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data from two different datasets. The results show that it can achieve better performance against other competing methods in terms of motion tracking accuracy and has the ability to learn biomechanical properties such as incompressibility and strains. The method has also been shown to have better generalisability to unseen domains compared with commonly used L2 regularisation schemes.Comment: The paper is early accepted by MICCAI 202

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.</p

Volumetric MRI Reconstruction from 2D Slices in the Presence of Motion

Despite recent advances in acquisition techniques and reconstruction algorithms, magnetic resonance imaging (MRI) remains challenging in the presence of motion. To mitigate this, ultra-fast two-dimensional (2D) MRI sequences are often used in clinical practice to acquire thick, low-resolution (LR) 2D slices to reduce in-plane motion. The resulting stacks of thick 2D slices typically provide high-quality visualizations when viewed in the in-plane direction. However, the low spatial resolution in the through-plane direction in combination with motion commonly occurring between individual slice acquisitions gives rise to stacks with overall limited geometric integrity. In further consequence, an accurate and reliable diagnosis may be compromised when using such motion-corrupted, thick-slice MRI data. This thesis presents methods to volumetrically reconstruct geometrically consistent, high-resolution (HR) three-dimensional (3D) images from motion-corrupted, possibly sparse, low-resolution 2D MR slices. It focuses on volumetric reconstructions techniques using inverse problem formulations applicable to a broad field of clinical applications in which associated motion patterns are inherently different, but the use of thick-slice MR data is current clinical practice. In particular, volumetric reconstruction frameworks are developed based on slice-to-volume registration with inter-slice transformation regularization and robust, complete-outlier rejection for the reconstruction step that can either avoid or efficiently deal with potential slice-misregistrations. Additionally, this thesis describes efficient Forward-Backward Splitting schemes for image registration for any combination of differentiable (not necessarily convex) similarity measure and convex (not necessarily smooth) regularization with a tractable proximal operator. Experiments are performed on fetal and upper abdominal MRI, and on historical, printed brain MR films associated with a uniquely long-term study dating back to the 1980s. The results demonstrate the broad applicability of the presented frameworks to achieve robust reconstructions with the potential to improve disease diagnosis and patient management in clinical practice

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 [electronic resource] : 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II /

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.Image Segmentation -- Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation -- Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound -- Unsupervised Quality Control of Image Segmentation based on Bayesian Learning -- One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation -- 'Project & Excite' Modules for Segmentation of Volumetric Medical Scans -- Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation -- Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation -- Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network -- Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation -- Instance Segmentation from Volumetric Biomedical Images without Voxel-Wise Labeling -- Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice -- Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation -- HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images -- PHiSeg: Capturing Uncertainty in Medical Image Segmentation -- Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data -- Supervised Uncertainty Quantification for Segmentation with Multiple Annotations -- 3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images -- Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation -- Statistical intensity- and shape-modeling to automate cerebrovascular segmentation from TOF-MRA data -- Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences using Contextual Memory -- Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion -- Mixed-Supervised Dual-Network for Medical Image Segmentation -- Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks -- Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation -- Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images -- Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation -- Radiomics-guided GAN for Segmentation of Liver Tumor without Contrast Agents -- Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks -- Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation -- Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss -- Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation -- Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation -- 3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation -- Impact of Adversarial Examples on Deep Learning Segmentation Models -- Multi-Resolution Path CNN with Deep Supervision for Intervertebral Disc Localization and Segmentation -- Automatic paraspinal muscle segmentation in patients with lumbar pathology using deep convolutional neural network -- Constrained Domain Adaptation for Segmentation -- Image Registration -- Image-and-Spatial Transformer Networks for Structure-Guided Image Registration -- Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration -- A deep learning approach to MR-less spatial normalization for tau PET images -- TopAwaRe: Topology-Aware Registration -- Multimodal Data Registration for Brain Structural Association Networks -- Dual-Stream Pyramid Registration Network -- A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration -- Conditional Segmentation in Lieu of Image Registration -- On the applicability of registration uncertainty -- DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation -- Linear Time Invariant Model based Motion Correction (LiMo-Moco) of Dynamic Radial Contrast Enhanced MRI -- Incompressible image registration using divergence-conforming B-splines -- Cardiovascular Imaging -- Direct Quantification for Coronary Artery Stenosis Using Multiview Learning -- Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization -- Discriminative Coronary Artery Tracking via 3D CNN in Cardiac CT Angiography -- Multi-modality Whole-Heart and Great Vessel Segmentation in Congenital Heart Disease using Deep Neural Networks and Graph Matching -- Harmonic Balance Techniques in Cardiovascular Fluid Mechanics -- Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking -- k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations -- Model-based reconstruction for highly accelerated first-pass perfusion cardiac MRI -- Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view images -- Right Ventricle Segmentation in Short-Axis MRI Using A Shape Constrained Dense Connected U-net -- Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction -- A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation -- Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors -- Curriculum semi-supervised segmentation -- A Multi-modal Network for Cardiomyopathy Death Risk Prediction with CMR Images and Clinical Information -- 3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata -- Discriminative Consistent Domain Generation for Semi-supervised Learning -- Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation -- MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation -- The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN -- Cardiac MRI Segmentation with Strong Anatomical Guarantees -- Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images -- Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets -- Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks -- Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation -- Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation -- Echocardiography View Classification Using Quality Transfer Star Generative Adversarial Networks -- Dual-view Joint Estimation of Left Ventricular Ejection Fraction with Uncertainty Modelling in Echocardiograms -- Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model -- Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images -- DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning -- Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries -- Fourier Spectral Dynamic Data Assimilation: Interlacing CFD with 4D flow MRI -- Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging -- HFA-Net: 3D Cardiovascular Image Segmentation with Asymmetrical Pooling and Content-Aware Fusion -- Spectral CT based training dataset generation and augmentation for conventional CT vascular segmentation -- Context-Aware Inductive Bias Learning for Vessel Border Detection in Multi-modal Intracoronary Imaging -- Growth, Development, Atrophy and Progression -- Neural parameters estimation for brain tumor growth modeling -- Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation -- Deep Probabilistic Modeling of Glioma Growth -- Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brains -- Variational Autoencoder for Regression: Application to Brain Aging Analysis -- Early Development of Infant Brain Complex Network -- Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties -- Continually Modeling Alzheimer's Disease Progression via Deep Multi-Order Preserving Weight Consolidation -- Disease Knowledge Transfer across Neurodegenerative Diseases.The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging