Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks

To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives.Comment: 13 pages, 7 figures, MIUA conferenc

The association between flow and oxygenation and cortical development in fetuses with congenital heart defects using a brain-age prediction algorithm.

OBJECTIVES: Presumably, changes in fetal circulation contribute to the delay in maturation of the cortex in fetuses with congenital heart defect (CHD). The aim of the current study is to analyze fetal brain development based on hemodynamic differences, using novel brain-age prediction software. METHODS: We have performed detailed neurosonography, including acquiring 3D volumes, prospectively in cases with isolated CHD from 20 weeks onwards. An algorithm that assesses the degree of fetal brain-age automatically was used to compare CHD cases to controls. We stratified CHD cases according to flow and oxygenation profiles by lesion physiology and performed subgroup analyses. RESULTS: A total of 616 ultrasound volumes of 162 CHD cases and 75 controls were analyzed. Significant differences in maturation of the cortex were observed in cases with normal blood flow toward the brain (-3.8 days, 95%CI [-5.5; -2.0], P = <.001) and low (-4.0 days, 95% CI [-6.7; -1.2] P = <.05; hypoplastic left heart syndrome[HLHS]) and mixed (-4.4 days, 95%CI [-6.4; -2.5] p = <.001) oxygen saturation in the ascending aorta (TGA) and in cardiac mixing (eg, Fallot) cases. CONCLUSION: The current study shows significant delay in brain-age in TGA and Fallot cases as compared to control cases. However, the small differences found in this study questions the clinical relevance

Cortical development in fetuses with congenital heart defects using an automated brain-age prediction algorithm.

INTRODUCTION: Congenital heart defects are associated with neurodevelopmental delay. It is hypothesized that fetuses affected by congenital heart defect have altered cerebral oxygen perfusion and are therefore prone to delay in cortical maturation. The aim of this study was to determine the difference in fetal brain age between consecutive congenital heart defect cases and controls in the second and third trimester using ultrasound. MATERIAL AND METHODS: Since 2014, we have included 90 isolated severe congenital heart defect cases in the Heart And Neurodevelopment (HAND)-study. Every 4 weeks, detailed neurosonography was performed in these fetuses, including the recording of a 3D volume of the fetal brain, from 20 weeks onwards. In all, 75 healthy fetuses underwent the same protocol to serve as a control group. The volumes were analyzed by automated age prediction software which determines gestational age by the assessment of cortical maturation. RESULTS: In total, 477 volumes were analyzed using the age prediction software (199 volumes of 90 congenital heart defect cases; 278 volumes of 75 controls). Of these, 16 (3.2%) volume recordings were excluded because of imaging quality. The age distribution was 19-33 weeks. Mixed model analysis showed that the age predicted by brain maturation was 3 days delayed compared with the control group (P = .002). CONCLUSIONS: This study shows that fetuses with isolated cases of congenital heart defects show some delay in cortical maturation as compared with healthy control cases. The clinical relevance of this small difference is debatable. This finding was consistent throughout pregnancy and did not progress during the third trimester

Deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound images

This paper concerns fully automatic and objective measurement of human skeletal muscle fiber orientation directly from standard b-mode ultrasound images using deep residual (ResNet) and convolutional neural networks (CNN). Fiber orientation and length is related with active and passive states of force production within muscle. There is currently no non-invasive way to measure force directly from muscle. Measurement of forces and other contractile parameters like muscle length change, thickness, and tendon length is not only important for understanding healthy muscle, but such information has contributed to understanding, diagnosis, monitoring, targeting and treatment of diseases ranging from myositis to stroke and motor neurone disease (MND). We applied well established deep learning methods to ultrasound data recorded from 19 healthy participants (5 female, ages: 30 ± 7.7) and achieved state of the art accuracy in predicting fiber orientation directly from ultrasound images of the calf muscles. First we used a previously developed segmentation technique to extract a region of interest within the gastrocnemius muscle. Then we asked an expert to annotate the main line of fiber orientation in 4 × 4 partitions of 400 normalized images. A linear model was then applied to the annotations to regulate and recover the orientation field for each image. Then we applied a CNN and a ResNet to predict the fiber orientation in each image. With leave one participant out cross-validation and dropout as a regulariser, we were able to demonstrate state of the art performance, recovering the fiber orientation with an average error of just 2°

Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years.

Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function1. We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women2, selected using World Health Organization recommendations for growth standards3. Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age4,5. The atlas was produced using 1,059 optimal quality, three-dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline6-8. The atlas corresponds structurally to published magnetic resonance images9, but with finer anatomical details in deep grey matter. The between-study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks' gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks' gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment

Omni-supervised learning: Scaling up to large unlabelled medical datasets

Two major bottlenecks in increasing algorithmic performance in the field of medical imaging analysis are the typically limited size of datasets and the shortage of expert labels for large datasets. This paper investigates approaches to overcome the latter via omni-supervised learning: a special case of semi-supervised learning. Our approach seeks to exploit a small annotated dataset and iteratively increase model performance by scaling up to refine the model using a large set of unlabelled data. By fusing predictions of perturbed inputs, the method generates new training annotations without human intervention. We demonstrate the effectiveness of the proposed framework to localize multiple structures in a 3D US dataset of 4044 fetal brain volumes with an initial expert annotation of just 200 volumes (5% in total) in training. Results show that structure localization error was reduced from 2.07 ± 1.65 mm to 1.76 ± 1.35 mm on the hold-out validation set

Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor

We present a general framework for automatic segmentation of fetal brain structures in ultrasound images inspired by recent advances in machine learning. The approach is based on a region descriptor that characterizes the shape and local intensity context of different neurological structures without explicit models. To validate our framework, we present experiments to segment two fetal brain structures of clinical importance that have quite different ultrasonic appearances—the corpus callosum (CC) and the choroid plexus (CP). Results demonstrate that our approach achieves high region segmentation accuracy (dice coefficient: 0.81% ± 0.06 CC, 0.76% ± 0.08 CP) relative to human delineation, whereas the derived automated biometry measurement deviations are within human intra/interobserver variations. The use of our proposed method may help to standardize intracranial anatomy measurements for both the routine examination and the detection of congenital conditions in the future

Robust regression of brain maturation from 3D fetal neurosonography using CRNs

We propose a fully three-dimensional Convolutional Regression Network (CRN) for the task of predicting fetal brain maturation from 3D ultrasound (US) data. Anatomical development is modelled as the sonographic patterns visible in the brain at a given gestational age, which are aggregated by the model into a single value: the brain maturation (BM) score. These patterns are learned from 589 3D fetal volumes, and the model is applied to 3D US images of 146 fetal subjects acquired at multiple, ethnically diverse sites, spanning an age range of 18 to 36 gestational weeks. Achieving a mean error of 7.7 days between ground-truth and estimated maturational scores, our method outperforms the current state-of-art for automated BM estimation from 3D US images

Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor

We present a general framework for automatic segmentation of fetal brain structures in ultrasound images inspired by recent advances in machine learning. The approach is based on a region descriptor that characterizes the shape and local intensity context of different neurological structures without explicit models. To validate our framework, we present experiments to segment two fetal brain structures of clinical importance that have quite different ultrasonic appearances—the corpus callosum (CC) and the choroid plexus (CP). Results demonstrate that our approach achieves high region segmentation accuracy (dice coefficient: 0.81% ± 0.06 CC, 0.76% ± 0.08 CP) relative to human delineation, whereas the derived automated biometry measurement deviations are within human intra/interobserver variations. The use of our proposed method may help to standardize intracranial anatomy measurements for both the routine examination and the detection of congenital conditions in the future