96 results found
Alansary A, Folgoc LL, Vaillant G, et al., 2018, Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
We propose a fully automatic method to find standardized view planes in 3Dimage acquisitions. Standard view images are important in clinical practice asthey provide a means to perform biometric measurements from similar anatomicalregions. These views are often constrained to the native orientation of a 3Dimage acquisition. Navigating through target anatomy to find the required viewplane is tedious and operator-dependent. For this task, we employ a multi-scalereinforcement learning (RL) agent framework and extensively evaluate severalDeep Q-Network (DQN) based strategies. RL enables a natural learning paradigmby interaction with the environment, which can be used to mimic experiencedoperators. We evaluate our results using the distance between the anatomicallandmarks and detected planes, and the angles between their normal vector andtarget. The proposed algorithm is assessed on the mid-sagittal andanterior-posterior commissure planes of brain MRI, and the 4-chamber long-axisplane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and4.84mm, respectively.
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, Khanal B, et al., 2018, 3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
CCBY Limited capture range, and the requirement to provide high quality initialization for optimization-based 2D/3D image registration methods, can significantly degrade the performance of 3D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion such as fetal in-utero imaging, complicate the 3D image and volume reconstruction process. In this paper we present a learning based image registration method capable of predicting 3D rigid transformations of arbitrarily oriented 2D image slices, with respect to a learned canonical atlas co-ordinate system. Only image slice intensity information is used to perform registration and canonical alignment, no spatial transform initialization is required. To find image transformations we utilize a Convolutional Neural Network (CNN) architecture to learn the regression function capable of mapping 2D image slices to a 3D canonical atlas space. We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline. Our learning based registration achieves an average spatial prediction error of 7 mm on simulated data and produces qualitatively improved reconstructions for heavily moving fetuses with gestational ages of approximately 20 weeks. Our model provides a general and computationally efficient solution to the 2D/3D registration initialization problem and is suitable for real-time scenarios.
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)
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
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
Recent advances in deep learning based image segmentation methods haveenabled real-time performance with human-level accuracy. However, occasionallyeven the best method fails due to low image quality, artifacts or unexpectedbehaviour of black box algorithms. Being able to predict segmentation qualityin the absence of ground truth is of paramount importance in clinical practice,but also in large-scale studies to avoid the inclusion of invalid data insubsequent analysis. In this work, we propose two approaches of real-time automated qualitycontrol for cardiovascular MR segmentations using deep learning. First, wetrain a neural network on 12,880 samples to predict Dice SimilarityCoefficients (DSC) on a per-case basis. We report a mean average error (MAE) of0.03 on 1,610 test samples and 97% binary classification accuracy forseparating low and high quality segmentations. Secondly, in the scenario whereno manually annotated data is available, we train a network to predict DSCscores from estimated quality obtained via a reverse testing strategy. Wereport an MAE=0.14 and 91% binary classification accuracy for this case.Predictions are obtained in real-time which, when combined with real-timesegmentation methods, enables instant feedback on whether an acquired scan isanalysable while the patient is still in the scanner. This further enables newapplications of optimising image acquisition towards best possible analysisresults.
Schlemper J, Oktay O, Chen L, et al., 2018, Attention-Gated Networks for Improving Ultrasound Scan Plane Detection.
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.
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
Alansary A, Kamnitsas K, Davidson A, et al., 2016, Fast fully automatic segmentation of the human placenta from motion corrupted MRI, Pages: 589-597, ISSN: 0302-9743
© Springer International Publishing AG 2016. Recently,magnetic resonance imaging has revealed to be important for the evaluation of placenta’s health during pregnancy. Quantitative assessment of the placenta requires a segmentation,which proves to be challenging because of the high variability of its position,orientation,shape and appearance. Moreover,image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus,as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field,so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20–38 weeks achieving a Dice score of 71.95 ± 19.79% for healthy fetuses with a fixed scan sequence and 66.89 ± 15.35% for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.
Baumgartner CF, Kamnitsas K, Matthew J, et al., 2016, Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks, Pages: 203-211, ISBN: 9783319467221
© Springer International Publishing AG 2016. Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However,there is a worldwide shortage of expert sonographers. In this paper,we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection,or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69% and 80%,which is superior to the current state-of-the-art. Furthermore,we show that it can retrospectively retrieve correct scan planes with an accuracy of 71% for cardiac views and 81% for non-cardiac views.
Baumgartner CH, Kamnitsas K, Matthew J, et al., 2016, Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks, International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 206, Publisher: Springer, Pages: 203-211
Fetal mid-pregnancy scans are typically carried out accordingto fixed protocols. Accurate detection of abnormalities and correctbiometric measurements hinge on the correct acquisition of clearlydefined standard scan planes. Locating these standard planes requires ahigh level of expertise. However, there is a worldwide shortage of expertsonographers. In this paper, we consider a fully automated system basedon convolutional neural networks which can detect twelve standard scanplanes as defined by the UK fetal abnormality screening programme. Thenetwork design allows real-time inference and can be naturally extendedto provide an approximate localisation of the fetal anatomy in the image.Such a framework can be used to automate or assist with scan planeselection, or for the retrospective retrieval of scan planes from recordedvideos. The method is evaluated on a large database of 1003 volunteermid-pregnancy scans. We show that standard planes acquired in a clinicalscenario are robustly detected with a precision and recall of 69 %and 80 %, which is superior to the current state-of-the-art. Furthermore,we show that it can retrospectively retrieve correct scan planes with anaccuracy of 71 % for cardiac views and 81 % for non-cardiac views.
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