Imperial College London

DR BERNHARD KAINZ

Faculty of EngineeringDepartment of Computing

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

78 results found

Egger J, Voglreiter P, Dokter M, Hofmann M, Busse HF, Seider D, Brandmaier P, Rautio R, Zettel G, Schmerböck B, van Amerongen M, Jenniskens S, Kolesnik M, Kainz B, Sequeiros RB, Portugaller H, Stiegler P, Futterer J, Schmalstieg D, Moche Met al., In-depth Multicenter Workflow Analysis of Liver Tumor Ablations for the Development of a Novel Computer-aided Software Tool, 101th Annual Meeting of The Radiological Society of North America (RSNA)

CONFERENCE PAPER

Hou B, Khanal B, Alansary A, McDonagh S, Davidson A, Rutherford M, Hajnal JV, Rueckert D, Glocker B, Kainz Bet al., 3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images

Limited capture range and the requirement to provide high qualityinitializations for optimization-based 2D/3D image registration methods cansignificantly degrade the per- formance of 3D image reconstruction and motioncompensation pipelines. Challenging clinical imaging scenarios, that containsig- nificant subject motion such as fetal in-utero imaging, complicate the 3Dimage and volume reconstruction process. In this paper we present a learningbased image registra- tion method capable of predicting 3D rigidtransformations of arbitrarily oriented 2D image slices, with respect to alearned canonical atlas co-ordinate system. Only image slice intensityinformation is used to perform registration and canonical align- ment, nospatial transform initialization is required. To find image transformations weutilize a Convolutional Neural Network (CNN) architecture to learn theregression function capable of mapping 2D image slices to the 3D canonicalatlas space. We extensively evaluate the effectiveness of our approachquantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brainimagery with synthetic motion and further demon- strate qualitative results onreal fetal MRI data where our method is integrated into a full reconstructionand motion compensation pipeline. Our learning based registration achieves anaverage spatial prediction error of 7 mm on simulated data and producesqualitatively improved reconstructions for heavily moving fetuses withgestational ages of approximately 20 weeks. Our model provides a general andcomputationally efficient solution to the 2D-3D registration initializationproblem and is suitable for real- time scenarios.

WORKING PAPER

KAINZ B, STREIT M, MUEHL J, MENDEZ Eet al., Seminar Paper Scene Graph Programming

JOURNAL ARTICLE

Kainz B, Lloyd D, Alansary A, Kuklisova Murgasova M, Khlebnikov R, Rueckert D, Rutherford M, Razavi R, Hajnal Jet al., High-Performance Motion Correction of Fetal MRI, EuroRVVV -- EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization, Publisher: Eurographics Digital Library

Fetal Magnetic Resonance Imaging (MRI) shows promising results for pre-natal diagnostics. The detection of potentially lifethreateningabnormalities in the fetus can be difficult with ultrasound alone. MRI is one of the few safe alternative imagingmodalities in pregnancy. However, to date it has been limited by unpredictable fetal and maternal motion during acquisition.Motion between the acquisitions of individual slices of a 3D volume results in spatial inconsistencies that can be resolved byslice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms to solve this problemhave evolved from very slow implementations targeting a single organ to general high-performance solutions to reconstruct thewhole uterus. In this paper we give a brief overview over the current state-of-the art in fetal motion compensation methods andshow currently emerging clinical applications of these techniques

CONFERENCE PAPER

Lloyd D, Kainz B, van Amerom J, Pushparajah KK, Simpson JM, Zidere V, Miller O, Sharland G, Zhang T, Lohezic M, Allsop J, Fox M, Malamateniou CH, Rutherford M, Hajnal JV, Razavi Ret al., Three-Dimensional Modelling of the Fetal Vasculature from Prenatal MRI using Motion-Corrected Slice-to-Volume Registration, International Society for Magnetic Resonance in Medicine Annual Meeting 2016

CONFERENCE PAPER

Miao H, Mistelbauer G, Kainz B, Placenta Maps: In Utero Placental Health Assessment of the Human Fetus, IEEE Pacific Visualization Symposium, ISSN: 2165-8765

CONFERENCE PAPER

Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook S, de Marvao A, Dawes T, O'Regan D, Kainz B, Glocker B, Rueckert Det al., Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

JOURNAL ARTICLE

Rajchl M, Lee MCH, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Damodaram M, Rutherford MA, Hajnal JV, Kainz B, Rueckert Det al., DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks, IEEE Transactions on Medical Imaging, ISSN: 1558-254X

In this paper, we proposeDeepCut, a method toobtain pixelwise object segmentations given an image datasetlabelled weak annotations, in our case bounding boxes. It extendsthe approach of the well-knownGrabCut[1] method to includemachine learning by training a neural network classifier frombounding box annotations. We formulate the problem as an en-ergy minimisation problem over a densely-connected conditionalrandom field and iteratively update the training targets to obtainpixelwise object segmentations. Additionally, we propose variantsof theDeepCutmethod and compare those to a na ̈ıve approach toCNN training under weak supervision. We test its applicabilityto solve brain and lung segmentation problems on a challengingfetal magnetic resonance dataset and obtain encouraging resultsin terms of accuracy.

JOURNAL ARTICLE

Reiter G, Reiter U, Kainz B, Greiser A, Horst B, Rienmüller Ret al., Appeared in, Journal of Cardiovascular Magnetic Resonance, Vol: 9

JOURNAL ARTICLE

Reiter U, Reiter G, Kovacs G, Schmidt K, Maier R, Kainz B, Olschewski H, Rienmueller Ret al., Magnetresonanz-basierte Messung des erhöhten, mittleren pulmonalarteriellen Drucks, RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Vol: 181, Pages: VO329\_6-VO329\_6

JOURNAL ARTICLE

Seider D, Kolesnik M, Kainz B, Payne S, Flanagan R, Pollari M, Stiegler P, Moche Met al., Entwicklung einer komplexen Softwareumgebung für die patientenspezifische Planung und Simulation der Radiofrequenzablation (RFA) von Lebertumoren, RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Vol: 185, Pages: VO301\_4-VO301\_4

JOURNAL ARTICLE

Alansary A, Rajchl M, McDonagh S, Murgasova M, Damodaram M, Lloyd DFA, Davidson A, Rutherford M, Hajnal JV, Rueckert D, Kainz Bet al., 2017, PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI, IEEE Transactions on Medical Imaging, ISSN: 1558-254X

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a \emph{single} investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in the presence of affine motion artifacts compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. We further evaluate the distance error for selected anatomical landmarks in the fetal head, as well as calculating the mean and maximum displacements resulting from automatic non-rigid registration to a motion-free ground truth image. These experiments demonstrate a successful application of PVR motion compensation to the whole fetal body, uterus and placenta.

JOURNAL ARTICLE

Alansary A, Rajchl M, McDonagh SG, Murgasova M, Damodaram M, Lloyd DFA, Davidson A, Rutherford M, Hajnal JV, Rueckert D, Kainz Bet al., 2017, PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI., IEEE Trans Med Imaging, Vol: 36, Pages: 2031-2044

In this paper, we present a novel method for the correction of motion artifacts that are present in fetal magnetic resonance imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patchwise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units, enabling its use in the clinical practice. We evaluate PVR's computational overhead compared with standard methods and observe improved reconstruction accuracy in the presence of affine motion artifacts compared with conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio, structural similarity index, and cross correlation with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. We further evaluate the distance error for selected anatomical landmarks in the fetal head, as well as calculating the mean and maximum displacements resulting from automatic non-rigid registration to a motion-free ground truth image. These experiments demonstrate a successful application of PVR motion compensation to the whole fetal body, uterus, and placenta.

JOURNAL ARTICLE

Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, Kainz B, Rueckert Det al., 2017, SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

CCBY Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09 & #x0025; accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8 & #x0025; was achieved on the localisation task.

JOURNAL ARTICLE

Cerrolaza JJ, Oktay O, Gomez A, Matthew J, Knight C, Kainz B, Rueckert Det al., 2017, Fetal skull segmentation in 3D ultrasound via structured geodesic random forest, 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.

CONFERENCE PAPER

Hou B, Alansary A, McDonagh S, Davidson A, Rutherford M, Hajnal JV, Rueckert D, Glocker B, Kainz Bet 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.

CONFERENCE PAPER

Kainz B, Bhatia K, Vaillant G, Zuluaga MAet al., 2017, Preface, ISBN: 9783319522791

BOOK

Kainz B, Bhatia K, Vercauteren T, 2017, Preface RAMBO 2017, ISBN: 9783319675633

BOOK

Miao H, Mistelbauer G, Karimov A, Alansary A, Davidson A, Lloyd DFA, Damodaram M, Story L, Hutter J, Hajnal JV, Rutherford M, Preim B, Kainz B, Groeller MEet 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

JOURNAL ARTICLE

Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook S, de Marvao A, Dawes T, O'Regan D, Kainz B, Glocker B, Rueckert Det al., 2017, Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation.

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.

WORKING PAPER

Rajchl M, Lee MCH, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Damodaram M, Rutherford MA, Hajnal JV, Kainz B, Rueckert Det 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

JOURNAL ARTICLE

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.

CONFERENCE PAPER

Alansary A, Kamnitsas K, Davidson A, Khlebnikov R, Rajchl M, Malamateniou C, Rutherford M, Hajnal JV, Glocker B, Rueckert D, Kainz Bet 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.

CONFERENCE PAPER

Baumgartner CF, Kamnitsas K, Matthew J, Smith S, Kainz B, Rueckert Det 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.

BOOK CHAPTER

Baumgartner CH, Kamnitsas K, Matthew J, Smith S, Kainz B, Rueckert Det 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.

CONFERENCE PAPER

Kainz B, Alansary A, McDonagh ST, Keraudren K, Kuklisova-Murgasova Met al., 2016, Fast motion compensation and super-resolution from multiple stacks of 2D slices

This tool implements a novel method for the correction of motion artifacts as acquired in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a defined amount of redundant information that is addressed with parallelized patch-wise optimization and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR’s computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments.Furthermore, we have verified our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.

SOFTWARE

Kainz B, Toisoul A, 2016, ShaderLab Framework

ShaderLab is a teaching tool to solidify the fundamentals of Computer Graphics. The ShaderLab framework is based on Qt5, CMake, OpenGL 4.0, and GLSL and allows the student to modify GLSL shaders in an IDE-like environment. The framework is able to render shaded polyhedral geometry (.off/.obj), supports image-based post-processing, and allows to implement simple ray-tracing algorithms. This tool will be intensively tested by 140 CO317 Computer Graphics students in Spring 2017.

SOFTWARE

Lloyd D, Kainz B, van Amerom JF, Lohezic M, Pushparajah K, Simpson JM, Malamateniou C, Hajnal JV, Rutherford M, Razavi Ret al., 2016, Prenatal MRI visualisation of the aortic arch and fetal vasculature using motion-corrected slice-to-volume reconstruction, Journal of Cardiovascular Magnetic Resonance, Vol: 18, Pages: P180-P180, ISSN: 1532-429X

JOURNAL ARTICLE

Rajchl M, Lee M, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Kainz B, Rueckert Det al., 2016, DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks, Publisher: arXiv:1605.07866

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naive approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.

OTHER

Rajchl M, Lee MCH, Schrans F, Davidson A, Passerat-Palmbach J, Tarroni G, Alansary A, Oktay O, Kainz B, Rueckert Det al., 2016, Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encounteredbottleneck of medical image analysis methods. While typically established by aclinical expert rater, the increase in acquired imaging data renderstraditional pixel-wise segmentations less feasible. In this paper, we examinethe use of a crowdsourcing platform for the distribution of super-pixel weakannotation tasks and collect such annotations from a crowd of non-expertraters. The crowd annotations are subsequently used for training a fullyconvolutional neural network to address the problem of fetal brain segmentationin T2-weighted MR images. Using this approach we report encouraging resultscompared to highly targeted, fully supervised methods and potentially address afrequent problem impeding image analysis research.

OTHER

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