744 results found
Bello G, Dawes T, Duan J, et al., 2019, Deep learning cardiac motion analysis for human survival prediction, Nature Machine Intelligence, Vol: 1, Pages: 95-104, ISSN: 2522-5839
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.
Gilbert K, Bai W, Mauger C, et al., 2019, Independent left ventricular morphometric atlases show consistent relationships with cardiovascular risk factors: A UK Biobank study, Scientific Reports, Vol: 9, ISSN: 2045-2322
Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric atlases enable precise quantification of shape and function, but there has been no objective comparison of different atlases in the same cohort. We compared two independent LV atlases using MRI scans of 4547 UK Biobank participants: (i) a volume atlas derived by automatic non-rigid registration of image volumes to a common template, and (ii) a surface atlas derived from manually drawn epicardial and endocardial surface contours. The strength of associations between atlas principal components and cardiovascular risk factors (smoking, diabetes, high blood pressure, high cholesterol and angina) were quantified with logistic regression models and five-fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) metrics. Both atlases exhibited similar principal components, showed similar relationships with risk factors, and had stronger associations (higher AUC and lower AIC) than a reference model based on LV mass and volume, for all risk factors (DeLong p < 0.05). Morphometric variations associated with each risk factor could be quantified and visualized and were similar between atlases. UK Biobank LV shape atlases are robust to construction method and show stronger relationships with cardiovascular risk factors than mass and volume.
Bastiani M, Andersson JLR, Cordero-Grande L, et al., 2019, Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project, NeuroImage, Vol: 185, Pages: 750-763, ISSN: 1053-8119
The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38-44 weeks post-menstrual age.
Meng Q, Pawlowski N, Rueckert D, et al., 2019, Representation disentanglement for multi-task learning with application to fetal ultrasound, Pages: 47-55, ISSN: 0302-9743
© 2019, Springer Nature Switzerland AG. One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms. In this paper we propose a novel representation disentanglement method to extract semantically meaningful and generalizable features for different tasks within a multi-task learning framework. Deep neural networks are utilized to ensure that the encoded features are maximally informative with respect to relevant tasks, while an adversarial regularization encourages these features to be disentangled and minimally informative about irrelevant tasks. We aim to use the disentangled representations to generalize the applicability of deep neural networks. We demonstrate the advantages of the proposed method on synthetic data as well as fetal ultrasound images. Our experiments illustrate that our method is capable of learning disentangled internal representations. It outperforms baseline methods in multiple tasks, especially on images with new properties, e.g. previously unseen artifacts in fetal ultrasound.
Schlemper J, Salehi SSM, Kundu P, et al., 2019, Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 57-64, ISSN: 0302-9743
Qin C, Schlemper J, Duan J, et al., 2019, k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 505-513, ISSN: 0302-9743
Seegoolam G, Schlemper J, Qin C, et al., 2019, Exploiting Motion for Deep Learning Reconstruction of Extremely-Undersampled Dynamic MRI, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 704-712, ISSN: 0302-9743
Vlontzos A, Alansary A, Kamnitsas K, et al., 2019, Multiple Landmark Detection Using Multi-agent Reinforcement Learning, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 262-270, ISSN: 0302-9743
Oksuz I, Clough J, Ruijsink B, et al., 2019, Detection and Correction of Cardiac MRI Motion Artefacts During Reconstruction from k-space, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 695-703, ISSN: 0302-9743
Wright R, Toussaint N, Gomez A, et al., 2019, Complete Fetal Head Compounding from Multi-view 3D Ultrasound, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 384-392, ISSN: 0302-9743
Qin C, Shi B, Liao R, et al., 2019, Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations, 26th Biennial International Conference on Information Processing in Medical Imaging (IPMI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 249-261, ISSN: 0302-9743
Qin C, Hajnal JV, Rueckert D, et al., 2019, Convolutional recurrent neural networks for dynamic MR image reconstruction, IEEE Transactions on Medical Imaging, Vol: 38, Pages: 280-290, ISSN: 0278-0062
Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI) leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The key ingredient to the problem is how to exploit the temporal correlations of the MR sequence to resolve aliasing artefacts. Traditionally, such observation led to a formulation of an optimisation problem, which was solved using iterative algorithms. Recently, however, deep learning based-approaches have gained significant popularity due to their ability to solve general inverse problems. In this work, we propose a unique, novel convolutional recurrent neural network (CRNN) architecture which reconstructs high quality cardiac MR images from highly undersampled k-space data by jointly exploiting the dependencies of the temporal sequences as well as the iterative nature of the traditional optimisation algorithms. In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations. In addition, spatio-temporal dependencies are simultaneously learnt by exploiting bidirectional recurrent hidden connections across time sequences. The proposed method is able to learn both the temporal dependency and the iterative reconstruction process effectively with only a very small number of parameters, while outperforming current MR reconstruction methods in terms of reconstruction accuracy and speed.
Knoll F, Maier A, Rueckert D, et al., 2019, Preface, ISBN: 9783030338428
Hou B, Vlontzos A, Alansary A, et al., 2019, Flexible Conditional Image Generation of Missing Data with Learned Mental Maps, Pages: 139-150, ISSN: 0302-9743
© Springer Nature Switzerland AG 2019. Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic. In clinical practice it is frequently common to acquire only sparse data (e.g. individual slices) for initial diagnostic decision making. Thereby, physicians rely on their prior knowledge (or mental maps) of the human anatomy to extrapolate the underlying 3D information. Accurate mental maps require years of anatomy training, which in the first instance relies on normative learning, i.e. excluding pathology. In this paper, we leverage Bayesian Deep Learning and environment mapping to generate full volumetric anatomy representations from none to a small, sparse set of slices. We evaluate proof of concept implementations based on Generative Query Networks (GQN) and Conditional BRUNO using abdominal CT and brain MRI as well as in a clinical application involving sparse, motion-corrupted MR acquisition for fetal imaging. Our approach allows to reconstruct 3D volumes from 1 to 4 tomographic slices, with a SSIM of 0.7+ and cross-correlation of 0.8+ compared to the 3D ground truth.
Ouyang C, Kamnitsas K, Biffi C, et al., 2019, Data Efficient Unsupervised Domain Adaptation For Cross-modality Image Segmentation, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 669-677, ISSN: 0302-9743
Kevin Zhou S, Fichtinger G, Rueckert D, 2019, Handbook of medical image computing and computer assisted intervention, ISBN: 9780128161760
© 2020 Elsevier Inc. All rights reserved. Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.
Schlemper J, Oktay O, Schaap M, et al., 2019, Attention gated networks: Learning to leverage salient regions in medical images., Medical Image Anal., Vol: 53, Pages: 197-207
Bruun M, Frederiksen KS, Rhodius-Meester HFM, et al., 2019, Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study., Curr Alzheimer Res, Vol: 16, Pages: 91-101
BACKGROUND: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. OBJECTIVES: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. METHODS: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. RESULTS: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). CONCLUSION: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians' confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.
Vlontzos A, Alansary A, Kamnitsas K, et al., 2019, Multiple Landmark Detection Using Multi-agent Reinforcement Learning., Publisher: Springer, Pages: 262-270
Hou B, Vlontzos A, Alansary A, et al., 2019, Flexible Conditional Image Generation of Missing Data with Learned Mental Maps., Publisher: Springer, Pages: 139-150
van Essen TA, den Boogert HF, Cnossen MC, et al., 2018, Variation in neurosurgical management of traumatic brain injury: A survey in 68 centers participating in the CENTER-TBI study, Acta Neurochirurgica, Vol: 161, Pages: 435-449, ISSN: 0001-6268
BackgroundNeurosurgical management of traumatic brain injury (TBI) is challenging, with only low-quality evidence. We aimed to explore differences in neurosurgical strategies for TBI across Europe.MethodsA survey was sent to 68 centers participating in the Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. The questionnaire contained 21 questions, including the decision when to operate (or not) on traumatic acute subdural hematoma (ASDH) and intracerebral hematoma (ICH), and when to perform a decompressive craniectomy (DC) in raised intracranial pressure (ICP).ResultsThe survey was completed by 68 centers (100%). On average, 10 neurosurgeons work in each trauma center. In all centers, a neurosurgeon was available within 30 min. Forty percent of responders reported a thickness or volume threshold for evacuation of an ASDH. Most responders (78%) decide on a primary DC in evacuating an ASDH during the operation, when swelling is present. For ICH, 3% would perform an evacuation directly to prevent secondary deterioration and 66% only in case of clinical deterioration. Most respondents (91%) reported to consider a DC for refractory high ICP. The reported cut-off ICP for DC in refractory high ICP, however, differed: 60% uses 25 mmHg, 18% 30 mmHg, and 17% 20 mmHg. Treatment strategies varied substantially between regions, specifically for the threshold for ASDH surgery and DC for refractory raised ICP. Also within center variation was present: 31% reported variation within the hospital for inserting an ICP monitor and 43% for evacuating mass lesions.ConclusionDespite a homogeneous organization, considerable practice variation exists of neurosurgical strategies for TBI in Europe. These results provide an incentive for comparative effectiveness research to determine elements of effective neurosurgical care.
Balaban G, Halliday BP, Costa CM, et al., 2018, Fibrosis Microstructure Modulates Reentry in Non-ischemic Dilated Cardiomyopathy: Insights From Imaged Guided 2D Computational Modeling, Frontiers in Physiology, Vol: 9, ISSN: 1664-042X
Aims: Patients who present with non-ischemic dilated cardiomyopathy (NIDCM) andenhancement on late gadolinium magnetic resonance imaging (LGE-CMR), are at highrisk of sudden cardiac death (SCD). Further risk stratification of these patients basedon LGE-CMR may be improved through better understanding of fibrosis microstructure.Our aim is to examine variations in fibrosis microstructure based on LGE imaging, andquantify the effect on reentry inducibility and mechanism. Furthermore, we examine therelationship between transmural activation time differences and reentry.Methods and Results: 2D Computational models were created from a single short axisLGE-CMR image, with 401 variations in fibrosis type (interstitial, replacement) and density,as well as presence or absence of reduced conductivity (RC). Transmural activationtimes (TAT) were measured, as well as reentry incidence and mechanism. Reentrieswere inducible above specific density thresholds (0.8, 0.6 for interstitial, replacementfibrosis). RC reduced these thresholds (0.3, 0.4 for interstitial, replacement fibrosis) andincreased reentry incidence (48 no RC vs. 133 with RC). Reentries were classified as rotor,micro-reentry, or macro-reentry and depended on fibrosis micro-structure. Differencesin TAT at coupling intervals 210 and 500ms predicted reentry in the models (sensitivity89%, specificity 93%). A sensitivity analysis of TAT and reentry incidence showed thatthese quantities were robust to small changes in the pacing location.Conclusion: Computational models of fibrosis micro-structure underlying areas ofLGE in NIDCM provide insight into the mechanisms and inducibility of reentry, andtheir dependence upon the type and density of fibrosis. Transmural activation times,measured at the central extent of the scar, can potentially differentiate microstructureswhich support reentry.
Duan J, Schlemper J, Bai W, et al., 2018, Combining deep learning and shape priors for bi-ventricular segmentation of volumetric cardiac magnetic resonance images, MICCAI ShapeMI Workshop, Publisher: Springer Verlag, Pages: 258-267, ISSN: 0302-9743
In this paper, we combine a network-based method with image registration to develop a shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance (CMR) volumetric images. The method first employs a fully convolutional network (FCN) to learn the segmentation task from manually labelled ground truth CMR volumes. However, due to the presence of image artefacts in the training dataset, the resulting FCN segmentation results are often imperfect. As such, we propose a second step to refine the FCN segmentation. This step involves performing a non-rigid registration with multiple high-resolution bi-ventricular atlases, allowing the explicit shape priors to be inferred. We validate the proposed approach on 1831 healthy subjects and 200 subjects with pulmonary hypertension. Numerical experiments on the two datasets demonstrate that our approach is capable of producing accurate, high-resolution and anatomically smooth bi-ventricular models, despite the artefacts in the input CMR volumes.
Dawes T, Simoes Monteiro de Marvao A, Shi W, et al., 2018, Identifying the optimal regional predictor of right ventricular global function: a high resolution 3D cardiac magnetic resonance study, Anaesthesia, Vol: 74, Pages: 312-320, ISSN: 0003-2409
Right ventricular (RV) function has prognostic value in acute, chronic and peri‐operative disease, although the complex RV contractile pattern makes rapid assessment difficult. Several two‐dimensional (2D) regional measures estimate RV function, however the optimal measure is not known. High‐resolution three‐dimensional (3D) cardiac magnetic resonance cine imaging was acquired in 300 healthy volunteers and a computational model of RV motion created. Points where regional function was significantly associated with global function were identified and a 2D, optimised single‐point marker (SPM‐O) of global function developed. This marker was prospectively compared with tricuspid annular plane systolic excursion (TAPSE), septum‐freewall displacement (SFD) and their fractional change (TAPSE‐F, SFD‐F) in a test cohort of 300 patients in the prediction of RV ejection fraction. RV ejection fraction was significantly associated with systolic function in a contiguous 7.3 cm2 patch of the basal RV freewall combining transverse (38%), longitudinal (35%) and circumferential (27%) contraction and coinciding with the four‐chamber view. In the test cohort, all single‐point surrogates correlated with RV ejection fraction (p < 0.010), but correlation (R) was higher for SPM‐O (R = 0.44, p < 0.001) than TAPSE (R = 0.24, p < 0.001) and SFD (R = 0.22, p < 0.001), and non‐significantly higher than TAPSE‐F (R = 0.40, p < 0.001) and SFD‐F (R = 0.43, p < 0.001). SPM‐O explained more of the observed variance in RV ejection fraction (19%) and predicted it more accurately than any other 2D marker (median error 2.8 ml vs 3.6 ml, p < 0.001). We conclude that systolic motion of the basal RV freewall predicts global function more accurately than other 2D estimators. However, no markers summarise 3D contractile patterns, limiting their predictive accuracy.
Bozek J, Makropoulos A, Schuh A, et al., 2018, Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project, NeuroImage, Vol: 179, Pages: 11-29, ISSN: 1053-8119
We propose a method for constructing a spatio-temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de-drifting the template. We used temporal adaptive kernel regression to produce age-dependant atlases for 9 weeks (36-44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age.
Li Y, Alansary A, Cerrolaza J, et al., 2018, Fast multiple landmark localisation using a patch-based iterative network, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer Verlag, ISSN: 0302-9743
We propose a new Patch-based Iterative Network (PIN) for fast and accuratelandmark localisation in 3D medical volumes. PIN utilises a ConvolutionalNeural Network (CNN) to learn the spatial relationship between an image patchand anatomical landmark positions. During inference, patches are repeatedlypassed to the CNN until the estimated landmark position converges to the truelandmark location. PIN is computationally efficient since the inference stageonly selectively samples a small number of patches in an iterative fashionrather than a dense sampling at every location in the volume. Our approachadopts a multi-task learning framework that combines regression andclassification to improve localisation accuracy. We extend PIN to localisemultiple landmarks by using principal component analysis, which models theglobal anatomical relationships between landmarks. We have evaluated PIN using72 3D ultrasound images from fetal screening examinations. PIN achievesquantitatively an average landmark localisation error of 5.59mm and a runtimeof 0.44s to predict 10 landmarks per volume. Qualitatively, anatomical 2Dstandard scan planes derived from the predicted landmark locations are visuallysimilar to the clinical ground truth.
Li Y, Khanal B, Hou B, et al., 2018, Standard plane detection in 3D fetal ultrasound using an iterative transformation network, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Publisher: Springer Verlag, Pages: 392-400, ISSN: 0302-9743
Standard scan plane detection in fetal brain ultrasound (US) forms a crucialstep in the assessment of fetal development. In clinical settings, this is doneby manually manoeuvring a 2D probe to the desired scan plane. With the adventof 3D US, the entire fetal brain volume containing these standard planes can beeasily acquired. However, manual standard plane identification in 3D volume islabour-intensive and requires expert knowledge of fetal anatomy. We propose anew Iterative Transformation Network (ITN) for the automatic detection ofstandard planes in 3D volumes. ITN uses a convolutional neural network to learnthe relationship between a 2D plane image and the transformation parametersrequired to move that plane towards the location/orientation of the standardplane in the 3D volume. During inference, the current plane image is passediteratively to the network until it converges to the standard plane location.We explore the effect of using different transformation representations asregression outputs of ITN. Under a multi-task learning framework, we introduceadditional classification probability outputs to the network to act asconfidence measures for the regressed transformation parameters in order tofurther improve the localisation accuracy. When evaluated on 72 US volumes offetal brain, our method achieves an error of 3.83mm/12.7 degrees and3.80mm/12.6 degrees for the transventricular and transcerebellar planesrespectively and takes 0.46s per plane.
Hou B, Miolane N, Khanal B, et al., 2018, Computing CNN loss and gradients for pose estimation with Riemannian geometry, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer Verlag, Pages: 756-764, ISSN: 0302-9743
Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis. Deep learning methods often parameterise poses with a representation that separates rotation and translation.As commonly available frameworks do not provide means to calculate loss on a manifold, regression is usually performed using the L2-norm independently on the rotation’s and the translation’s parameterisations. This is a metric for linear spaces that does not take into account the Lie group structure of SE(3). In this paper, we propose a general Riemannian formulation of the pose estimation problem, and train CNNs directly on SE(3) equipped with a left-invariant Riemannian metric. The loss between the ground truth and predicted pose (elements of the manifold) is calculated as the Riemannian geodesic distance, which couples together the translation and rotation components. Network weights are updated by back-propagating the gradient with respect to the predicted pose on the tangent space of the manifold SE(3). We thoroughly evaluate the effectiveness of our loss function by comparing its performance with popular and most commonly used existing methods, on tasks such as image-based localisation and intensity-based 2D/3D registration. We also show that hyper-parameters, used in our loss function to weight the contribution between rotations andtranslations, can be intrinsically calculated from the dataset to achievegreater performance margins.
Alansary A, Le Folgoc L, Vaillant G, et al., 2018, Automatic view planning with multi-scale deep reinforcement learning agents, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer Verlag, Pages: 277-285, ISSN: 0302-9743
We propose a fully automatic method to find standardizedview planes in 3D image acquisitions. Standard view images are impor-tant in clinical practice as they provide a means to perform biometricmeasurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several DeepQ-Network (DQN) based strategies. RL enables a natural learning paradigm by interaction with the environment, which can be used to mimic experienced operators. We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and 4.84mm, respectively.
Schlemper J, Yang G, Ferreira P, et al., 2018, Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 11070 LNCS, Pages: 295-303, ISSN: 0302-9743
© Springer Nature Switzerland AG 2018. Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the investigation of effective therapeutics. Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive technique that can resolve the microscopic structure, organisation, and integrity of the myocardium without the need for exogenous contrast agents. However, this technique suffers from relatively low signal-to-noise ratio (SNR) and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR techniques rely on acquiring and averaging multiple signal acquisitions to improve the SNR. Moreover, in order to mitigate the influence of respiratory movement, patients are required to perform many breath holds which results in prolonged acquisition durations (e.g., ~ 30 min using the existing technology). In this study, we propose a novel cascaded Convolutional Neural Networks (CNN) based compressive sensing (CS) technique and explore its applicability to improve DT-CMR acquisitions. Our simulation based studies have achieved high reconstruction fidelity and good agreement between DT-CMR parameters obtained with the proposed reconstruction and fully sampled ground truth. When compared to other state-of-the-art methods, our proposed deep cascaded CNN method and its stochastic variation demonstrated significant improvements. To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction. In addition, with relatively straightforward modifications to the acquisition scheme, our method can easily be translated into a method for online, at-the-scanner reconstruction enabling the deployment of accelerated DT-CMR in various clinical applications.
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