Isointense infant brain MRI segmentation with a dilated convolutional neural network

Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.Comment: MICCAI grand challenge on 6-month infant brain MRI segmentatio

Exploring the similarity of medical imaging classification problems

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3\% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community

Domain-adversarial neural networks to address the appearance variability of histopathology images

Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information from the model representation leads to better generalization. We tested our hypothesis for the problem of mitosis detection in breast cancer histopathology images and made a comparative analysis with two other approaches. We show that combining color augmentation with domain-adversarial training is a better alternative than standard approaches to improve the generalization of deep learning methods.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi

Inferring a Third Spatial Dimension from 2D Histological Images

Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast. This process results in 2D images of the specimen that has a three-dimensional structure. In this paper, we propose a method to infer how the stains are distributed in the direction perpendicular to the surface of the slide for a given 2D image in order to obtain a 3D representation of the tissue. This inference is achieved by decomposition of the staining concentration maps under constraints that ensure realistic decomposition and reconstruction of the original 2D images. Our study shows that it is possible to generate realistic 3D images making this method a potential tool for data augmentation when training deep learning models.Comment: IEEE International Symposium on Biomedical Imaging (ISBI), 201

Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol

Automatic MRI-based quantication of brain characteristics in preterm newborns

Even though survival of preterm infants has improved in recent years, preterm birth is still associated with developmental impairments, such as cognitive and behavioural problems. Brain development is particularly vulnerable in this population because an important part of development takes place after birth, which could lead to primary brain injury and secondary developmental consequences. Especially the cerebral cortex rapidly develops from a smooth surface to a complexly folded structure in the third trimester of pregnancy and is therefore vulnerable in extra-uterine conditions. Cortical development has also been described to be disturbed by white matter injury. Magnetic resonance imaging (MRI) provides an important non-invasive tool to assess brain development in preterm infants. This thesis describes an automated system for quantification of brain characteristics in preterm newborns based on MR brain images. To allow automatic quantification of brain characteristics based on these images, Chapters 2–5 describe automatic segmentation (i.e. labelling of regions of interest in the image) approaches for MR brain images. Chapter 2 describes an automatic segmentation method for cortical grey matter, unmyelinated white matter and extracerebral cerebrospinal fluid. The method is based on sequential supervised voxel classification and evaluated on coronal MR images of preterm newborns at 30 and 40 weeks postmenstrual age. Chapter 3 evaluates the approach described in Chapter 2 in the segmentation in MR images of ageing adults at an average age of 70 years. Chapter 4 describes an automatic segmentation method that uses a multiscale convolutional neural network to segment neonatal and adult MR brain images into a number of tissue classes. Chapter 5 describes a general medical image segmentation method using a convolutional neural network that is applied to the segmentation of seven tissue classes in MR brain images, the pectoral muscle in MR breast images and the coronary arteries in cardiac CT angiography. Based on automatic segmentations or MR brain images, Chapter 6 describes a system to compute characteristics of the cerebral cortex. The system is evaluated in a cohort of 85 preterm newborns imaged at 30 and 40 weeks postmenstrual age. The descriptors show longitudinal and regional differences, and show a relation with a conventional visual brain abnormality scoring system. Chapter 7 describes cortical morphology in newborns with severe congenital heart disease compared with healthy controls, using the system from Chapter 6. Chapter 8 describes prediction of cognitive and motor performance at later age based on descriptors automatically computed from MR images of preterm newborns, using the segmentation approach from Chapter 4 and the feature computation approach from Chapter 6. The work in this thesis showed that it is possible to automatically compute quantitative descriptors from an MR brain image that are valuable in the assessment of neurodevelopment of preterm infants: from image segmentation, to feature computation, to outcome prediction. Such a system could in the future be implemented in clinical practice to, directly after the image acquisition, compute quantitative measurements and possibly even identify preterm infants at risk of neurodevelopmental impairments

Automatic MRI-based quantication of brain characteristics in preterm newborns

Even though survival of preterm infants has improved in recent years, preterm birth is still associated with developmental impairments, such as cognitive and behavioural problems. Brain development is particularly vulnerable in this population because an important part of development takes place after birth, which could lead to primary brain injury and secondary developmental consequences. Especially the cerebral cortex rapidly develops from a smooth surface to a complexly folded structure in the third trimester of pregnancy and is therefore vulnerable in extra-uterine conditions. Cortical development has also been described to be disturbed by white matter injury. Magnetic resonance imaging (MRI) provides an important non-invasive tool to assess brain development in preterm infants. This thesis describes an automated system for quantification of brain characteristics in preterm newborns based on MR brain images. To allow automatic quantification of brain characteristics based on these images, Chapters 2–5 describe automatic segmentation (i.e. labelling of regions of interest in the image) approaches for MR brain images. Chapter 2 describes an automatic segmentation method for cortical grey matter, unmyelinated white matter and extracerebral cerebrospinal fluid. The method is based on sequential supervised voxel classification and evaluated on coronal MR images of preterm newborns at 30 and 40 weeks postmenstrual age. Chapter 3 evaluates the approach described in Chapter 2 in the segmentation in MR images of ageing adults at an average age of 70 years. Chapter 4 describes an automatic segmentation method that uses a multiscale convolutional neural network to segment neonatal and adult MR brain images into a number of tissue classes. Chapter 5 describes a general medical image segmentation method using a convolutional neural network that is applied to the segmentation of seven tissue classes in MR brain images, the pectoral muscle in MR breast images and the coronary arteries in cardiac CT angiography. Based on automatic segmentations or MR brain images, Chapter 6 describes a system to compute characteristics of the cerebral cortex. The system is evaluated in a cohort of 85 preterm newborns imaged at 30 and 40 weeks postmenstrual age. The descriptors show longitudinal and regional differences, and show a relation with a conventional visual brain abnormality scoring system. Chapter 7 describes cortical morphology in newborns with severe congenital heart disease compared with healthy controls, using the system from Chapter 6. Chapter 8 describes prediction of cognitive and motor performance at later age based on descriptors automatically computed from MR images of preterm newborns, using the segmentation approach from Chapter 4 and the feature computation approach from Chapter 6. The work in this thesis showed that it is possible to automatically compute quantitative descriptors from an MR brain image that are valuable in the assessment of neurodevelopment of preterm infants: from image segmentation, to feature computation, to outcome prediction. Such a system could in the future be implemented in clinical practice to, directly after the image acquisition, compute quantitative measurements and possibly even identify preterm infants at risk of neurodevelopmental impairments

Automatic MRI-based quantication of brain characteristics in preterm newborns

Even though survival of preterm infants has improved in recent years, preterm birth is still associated with developmental impairments, such as cognitive and behavioural problems. Brain development is particularly vulnerable in this population because an important part of development takes place after birth, which could lead to primary brain injury and secondary developmental consequences. Especially the cerebral cortex rapidly develops from a smooth surface to a complexly folded structure in the third trimester of pregnancy and is therefore vulnerable in extra-uterine conditions. Cortical development has also been described to be disturbed by white matter injury. Magnetic resonance imaging (MRI) provides an important non-invasive tool to assess brain development in preterm infants. This thesis describes an automated system for quantification of brain characteristics in preterm newborns based on MR brain images. To allow automatic quantification of brain characteristics based on these images, Chapters 2–5 describe automatic segmentation (i.e. labelling of regions of interest in the image) approaches for MR brain images. Chapter 2 describes an automatic segmentation method for cortical grey matter, unmyelinated white matter and extracerebral cerebrospinal fluid. The method is based on sequential supervised voxel classification and evaluated on coronal MR images of preterm newborns at 30 and 40 weeks postmenstrual age. Chapter 3 evaluates the approach described in Chapter 2 in the segmentation in MR images of ageing adults at an average age of 70 years. Chapter 4 describes an automatic segmentation method that uses a multiscale convolutional neural network to segment neonatal and adult MR brain images into a number of tissue classes. Chapter 5 describes a general medical image segmentation method using a convolutional neural network that is applied to the segmentation of seven tissue classes in MR brain images, the pectoral muscle in MR breast images and the coronary arteries in cardiac CT angiography. Based on automatic segmentations or MR brain images, Chapter 6 describes a system to compute characteristics of the cerebral cortex. The system is evaluated in a cohort of 85 preterm newborns imaged at 30 and 40 weeks postmenstrual age. The descriptors show longitudinal and regional differences, and show a relation with a conventional visual brain abnormality scoring system. Chapter 7 describes cortical morphology in newborns with severe congenital heart disease compared with healthy controls, using the system from Chapter 6. Chapter 8 describes prediction of cognitive and motor performance at later age based on descriptors automatically computed from MR images of preterm newborns, using the segmentation approach from Chapter 4 and the feature computation approach from Chapter 6. The work in this thesis showed that it is possible to automatically compute quantitative descriptors from an MR brain image that are valuable in the assessment of neurodevelopment of preterm infants: from image segmentation, to feature computation, to outcome prediction. Such a system could in the future be implemented in clinical practice to, directly after the image acquisition, compute quantitative measurements and possibly even identify preterm infants at risk of neurodevelopmental impairments

Deformable image registration using convolutional neural networks

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization