Combining Shape and Learning for Medical Image Analysis

Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today\u27s automatic methods succeed to meet these requirements.\ua0The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields

Spatial Registration Evaluation of [18F]-MK6240 PET

Image registration is an important preprocessing step in neuroimaging which allows for the matching of anatomical and functional information between modalities and subjects. This can be challenging if there are gross differences in image geometry or in signal intensity, such as in the case of some molecular PET radioligands, where control subjects display relative lack of signal relative to noise within intracranial regions, and may have off target binding that may be confused as other regions, and may vary depending on subject. The use of intermediary images or volumes have been shown to aide registration in such cases. To account for this phenomena within our own longitudinal aging cohort, we generated a population specific MRI and PET template from a broad distribution of 30 amyloid negative subjects. We then registered the PET image of each of these subjects, as well as a holdout set of thirty 'template-naive' subjects to their corresponding MRI images using the template image as an intermediate using three different sets of registration parameters and procedures. To evaluate the performance of both conventional registration and our method, we compared these to the registration of the attenuation CT (acquired at time of PET acquisition) to MRI as the reference. We then used our template to directly derive SUVR values without the use of MRI. We found that conventional registration was comparable to an existing CT based standard, and there was no significant difference in errors collectively amongst all methods tested. In addition, there were no significant differences between existing and MR-less tau PET quantification methods. We conclude that a template-based method is a feasible alternative to, or salvage for, direct registration and MR-less quantification; and, may be preferred in cases where there is doubt about the similarity between two image modalities

A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images

The procedure of aligning a positron emission tomography (PET) image with a common coordinate system, spatial normalization, typically demands a corresponding structural magnetic resonance (MR) image. However, MR imaging is not always available or feasible for the subject, which calls for enabling spatial normalization without MR, MR-less spatial normalization. In this work, we propose a template-free approach to MR-less spatial normalization for [18F]flortaucipir tau PET images. We use a deep neural network that estimates an aligning transformation from the PET input image, and outputs the spatially normalized image as well as the parameterized transformation. In order to do so, the proposed network iteratively estimates a set of rigid and affine transformations by means of convolutional neural network regressors as well as spatial transformer layers. The network is trained and validated on 199 tau PET volumes with corresponding ground truth transformations, and tested on two different datasets. The proposed method shows competitive performance in terms of registration accuracy as well as speed, and compares favourably to previously published results

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