102 results found
Alansary A, Folgoc LL, Vaillant G, et al., 2018, Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents.
Hou B, Kainz B, 2018, DeepPose
A general Riemannian formulation of the pose estimation problem to train CNNs directly on SE(3) equipped with a left-invariant Riemannian metric.
Hou B, Khanal B, Alansary A, et al., 2018, 3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 1737-1750, ISSN: 0278-0062
Hou B, Miolane N, Khanal B, et al., 2018, Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry.
Kamnitsas K, Bai W, Ferrante E, et al., 2018, Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation, 3rd International Workshop on Brain-Lesion (BrainLes) held jointly at the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 450-462, ISSN: 0302-9743
Li Y, Alansary A, Cerrolaza JJ, et al., 2018, Fast Multiple Landmark Localisation Using a Patch-based Iterative Network.
Li Y, Khanal B, Hou B, et al., 2018, Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network.
Lloyd DFA, van Poppel M, Schultz A, et al., 2018, MOTION CORRECTED FETAL CARDIAC MRI INCREASES DIAGNOSTIC CONFIDENCE IN CLINICALLY CHALLENGING CASES, Annual Meeting of the British-Congenital-Cardiac-Association, Publisher: BMJ PUBLISHING GROUP, Pages: A11-A11, ISSN: 1355-6037
Oktay O, Ferrante E, Kamnitsas K, et al., 2018, Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 384-395, ISSN: 0278-0062
Oktay O, Schlemper J, Folgoc LL, et al., 2018, Attention U-Net: Learning Where to Look for the Pancreas.
Otkay O, Schlemper J, Kainz B, 2018, Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
Pytorch implementation of attention gates used in U-Net and VGG-16 models. The framework can be utilised in both medical image classification and segmentation tasks.
Robinson R, Oktay O, Bai W, et al., 2018, Real-time Prediction of Segmentation Quality.
Schlemper J, Oktay O, Chen L, et al., 2018, Attention-Gated Networks for Improving Ultrasound Scan Plane Detection.
Schlemper J, Oktay O, Schaap M, et al., 2018, Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images.
Verbruggen SW, Kainz B, Shelmerdine SC, et al., 2018, Altered biomechanical stimulation of the developing hip joint in presence of hip dysplasia risk factors., J Biomech, Vol: 78, Pages: 1-9
Fetal kicking and movements generate biomechanical stimulation in the fetal skeleton, which is important for prenatal musculoskeletal development, particularly joint shape. Developmental dysplasia of the hip (DDH) is the most common joint shape abnormality at birth, with many risk factors for the condition being associated with restricted fetal movement. In this study, we investigate the biomechanics of fetal movements in such situations, namely fetal breech position, oligohydramnios and primiparity (firstborn pregnancy). We also investigate twin pregnancies, which are not at greater risk of DDH incidence, despite the more restricted intra-uterine environment. We track fetal movements for each of these situations using cine-MRI technology, quantify the kick and muscle forces, and characterise the resulting stress and strain in the hip joint, testing the hypothesis that altered biomechanical stimuli may explain the link between certain intra-uterine conditions and risk of DDH. Kick force, stress and strain were found to be significantly lower in cases of breech position and oligohydramnios. Similarly, firstborn fetuses were found to generate significantly lower kick forces than non-firstborns. Interestingly, no significant difference was observed in twins compared to singletons. This research represents the first evidence of a link between the biomechanics of fetal movements and the risk of DDH, potentially informing the development of future preventative measures and enhanced diagnosis. Our results emphasise the importance of ultrasound screening for breech position and oligohydramnios, particularly later in pregnancy, and suggest that earlier intervention to correct breech position through external cephalic version could reduce the risk of hip dysplasia.
Verbruggen SW, Kainz B, Shelmerdine SC, et al., 2018, Stresses and strains on the human fetal skeleton during development, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 15, ISSN: 1742-5689
Alansary A, Rajchl M, McDonagh SG, et al., 2017, PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 36, Pages: 2031-2044, ISSN: 0278-0062
Bai W, Sinclair M, Tarroni G, et al., 2017, Human-level CMR image analysis with deep fully convolutional networks., CoRR, Vol: abs/1710.09289
Baumgartner CF, Kamnitsas K, Matthew J, et al., 2017, SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 36, Pages: 2204-2215, ISSN: 0278-0062
Cerrolaza JJ, Oktay O, Gomez A, et al., 2017, Fetal skull segmentation in 3D ultrasound via structured geodesic random forest, Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, Held in Conjunction with MICCAI 2017, Pages: 25-32, ISSN: 0302-9743
© Springer International Publishing AG 2017. Ultrasound is the primary imaging method for prenatal screening and diagnosis of fetal anomalies. Thanks to its non-invasive and non-ionizing properties, ultrasound allows quick, safe and detailed evaluation of the unborn baby, including the estimation of the gestational age, brain and cranium development. However, the accuracy of traditional 2D fetal biometrics is dependent on operator expertise and subjectivity in 2D plane finding and manual marking. 3D ultrasound has the potential to reduce the operator dependence. In this paper, we propose a new random forest-based segmentation framework for fetal 3D ultrasound volumes, able to efficiently integrate semantic and structural information in the classification process. We introduce a new semantic features space able to encode spatial context via generalized geodesic distance transform. Unlike alternative auto-context approaches, this new set of features is efficiently integrated into the same forest using contextual trees. Finally, we use a new structured labels space as alternative to the traditional atomic class labels, able to capture morphological variability of the target organ. Here, we show the potential of this new general framework segmenting the skull in 3D fetal ultrasound volumes, significantly outperforming alternative random forest-based approaches.
Hou B, Alansary A, McDonagh S, et al., 2017, Predicting slice-to-volume transformation in presence of arbitrary subject motion, Pages: 296-304, ISSN: 0302-9743
© Springer International Publishing AG 2017. This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotations and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7 mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography (CT) and X-Ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios.
Kainz B, Bhatia K, Vercauteren T, 2017, Preface RAMBO 2017, ISBN: 9783319675633
McDonagh S, Hou B, Alansary A, et al., 2017, Context-sensitive super-resolution for fast fetal magnetic resonance imaging, Pages: 116-126, ISSN: 0302-9743
© 2017, Springer International Publishing AG. 3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but low-resolution image acquisition and highly detailed but slow image acquisition. Fast imaging is required for targets that move to avoid motion artefacts. This is in particular difficult for fetal MRI. Spatially independent upsampling techniques, which are the state-of-the-art to address this problem, are error prone and disregard contextual information. In this paper we propose a context-sensitive upsampling method based on a residual convolutional neural network model that learns organ specific appearance and adopts semantically to input data allowing for the generation of high resolution images with sharp edges and fine scale detail. By making contextual decisions about appearance and shape, present in different parts of an image, we gain a maximum of structural detail at a similar contrast as provided by high-resolution data. We experiment on 145 fetal scans and show that our approach yields an increased PSNR of 1.25 dB when applied to under-sampled fetal data cf. baseline upsampling. Furthermore, our method yields an increased PSNR of 1.73 dB when utilizing under-sampled fetal data to perform brain volume reconstruction on motion corrupted captured data.
McDonagh SG, Hou B, Alansary A, et al., 2017, Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging., Publisher: Springer, Pages: 116-126
Miao H, Mistelbauer G, Karimov A, et al., 2017, Placenta Maps: In Utero Placental Health Assessment of the Human Fetus, IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, Vol: 23, Pages: 1612-1623, ISSN: 1077-2626
The human placenta is essential for the supply of the fetus. To monitor the fetal development, imaging data is acquired usingultrasound (US). Although it is currently the gold-standard in fetal imaging, it might not capture certain abnormalities of the placenta.Magnetic resonance imaging (MRI) is a safe alternative for the in utero examination while acquiring the fetus data in higher detail.Nevertheless, there is currently no established procedure for assessing the condition of the placenta and consequently the fetal health.Due to maternal respiration and inherent movements of the fetus during examination, a quantitative assessment of the placenta requiresfetal motion compensation, precise placenta segmentation and a standardized visualization, which are challenging tasks. Utilizingadvanced motion compensation and automatic segmentation methods to extract the highly versatile shape of the placenta, we introducea novel visualization technique that presents the fetal and maternal side of the placenta in a standardized way. Our approach enablesphysicians to explore the placenta even in utero. This establishes the basis for a comparative assessment of multiple placentas to analyzepossible pathologic arrangements and to support the research and understanding of this vital organ. Additionally, we propose athree-dimensional structure-aware surface slicing technique in order to explore relevant regions inside the placenta. Finally, to survey theapplicability of our approach, we consulted clinical experts in prenatal diagnostics and i
Pawlowski N, Ktena SI, Lee MCH, et al., 2017, DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images.
Rajchl M, Lee MCH, Oktay O, et al., 2017, DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 36, Pages: 674-683, ISSN: 0278-0062
Toisoul A, Rueckert D, Kainz B, 2017, Accessible GLSL Shader programming, EuroGraphics 2017, ISSN: 1017-4656
Teaching fundamental principles of Computer Graphics requires a thoroughly prepared lecture alongside practical training.Modern graphics programming rarely provides a straightforward application programming interface (API) and the availableAPIs pose high entry barriers to students. Shader-based programming of standard graphics pipelines is often inaccessiblethrough complex setup procedures and convoluted programming environments. In this paper we discuss an undergraduateentry level lecture with its according lab exercises. We present a programming framework that makes interactive graphicsprogramming accessible while allowing to design individual tasks as instructive exercises to solidify the content of individuallecture units. The discussed teaching framework provides a well defined programmable graphics pipeline with geometry shadingstages and image-based post processing functionality based on framebuffer objects. It is open-source and available online.
Alansary A, Kainz B, Rajchl M, et al., 2016, PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI., CoRR, Vol: abs/1611.07289
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