Alice Davidson

Bio: Alice Davidson is an academic researcher from King's College London. The author has contributed to research in topics: Motion compensation & Initialization. The author has an hindex of 9, co-authored 15 publications receiving 332 citations.

Normative biometry of the fetal brain using magnetic resonance imaging.

Abstract: The fetal brain shows accelerated growth in the latter half of gestation, and these changes can be captured by 2D and 3D biometry measurements. The aim of this study was to quantify brain growth in normal fetuses using Magnetic Resonance Imaging (MRI) and to produce reference biometry data and a freely available centile calculator ( https://www.developingbrain.co.uk/fetalcentiles/ ). A total of 127 MRI examinations (1.5 T) of fetuses with a normal brain appearance (21-38 gestational weeks) were included in this study. 2D and 3D biometric parameters were measured from slice-to-volume reconstructed images, including 3D measurements of supratentorial brain tissue, lateral ventricles, cortex, cerebellum and extra-cerebral CSF and 2D measurements of brain biparietal diameter and fronto-occipital length, skull biparietal diameter and occipitofrontal diameter, head circumference, transverse cerebellar diameter, extra-cerebral CSF, ventricular atrial diameter, and vermis height, width, and area. Centiles were constructed for each measurement. All participants were invited for developmental follow-up. All 2D and 3D measurements, except for atrial diameter, showed a significant positive correlation with gestational age. There was a sex effect on left and total lateral ventricular volumes and the degree of ventricular asymmetry. The 5th, 50th, and 95th centiles and a centile calculator were produced. Developmental follow-up was available for 73.1% of cases [mean chronological age 27.4 (±10.2) months]. We present normative reference charts for fetal brain MRI biometry at 21-38 gestational weeks. Developing growth trajectories will aid in the better understanding of normal fetal brain growth and subsequently of deviations from typical development in high-risk pregnancies or following premature delivery.

Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI

Abstract: 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 \(71.95\pm 19.79\,\%\) for healthy fetuses with a fixed scan sequence and \(66.89\pm 15.35\,\%\) for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.

3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images

Abstract: Limited capture range, and the requirement to provide high quality initialization for optimization-based 2-D/3-D image registration methods, can significantly degrade the performance of 3-D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion, such as fetal in-utero imaging, complicate the 3-D image and volume reconstruction process. In this paper, we present a learning-based image registration method capable of predicting 3-D rigid transformations of arbitrarily oriented 2-D 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 architecture to learn the regression function capable of mapping 2-D image slices to a 3-D 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 2-D/3-D registration initialization problem and is suitable for real-time scenarios.

Predicting slice-to-volume transformation in presence of arbitrary subject motion

Abstract: 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.

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

Abstract: 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.

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Abstract: Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.

DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

Abstract: In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut[1] 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.