Atlas-based segmentation and classification of magnetic resonance brain images

A wide range of different image modalities can be found today in medical imaging. These modalities allow the physician to obtain a non-invasive view of the internal organs of the human body, such as the brain. All these three dimensional images are of extreme importance in several domains of medicine, for example, to detect pathologies, follow the evolution of these pathologies, prepare and realize surgical planning with, or without, the help of robot systems or for statistical studies. Among all the medical image modalities, Magnetic Resonance (MR) imaging has become of great interest in many research areas due to its great spatial and contrast image resolution. It is therefore perfectly suited for anatomic visualization of the human body such as deep structures and tissues of the brain. Medical image analysis is a complex task because medical images usually involve a large amount of data and they sometimes present some undesirable artifacts, as for instance the noise. However, the use of a priori knowledge in the analysis of these images can greatly simplify this task. This prior information is usually represented by the reference images or atlases. Modern brain atlases are derived from high resolution cryosections or in vivo images, single subject-based or population-based, and they provide detailed images that may be interactively and easily examined in their digital format in computer assisted diagnosis or intervention. Then, in order to efficiently combine all this information, a battery of registration techniques is emerging based on transformations that bring two medical images into voxel-to-voxel correspondence. One of the main aims of this thesis is to outline the importance of including prior knowledge in the medical image analysis framework and the indispensable role of registration techniques in this task. In order to do that, several applications using atlas information are presented. First, the atlas-based segmentation in normal anatomy is shown as it is a key application of medical image analysis using prior knowledge. It consists of registering the brain images derived from different subjects and modalities within the atlas coordinate system to improve the localization and delineation of the structures of interest. However, the use of an atlas can be problematic in some particular cases where some structures, for instance a tumor or a sulcus, exists in the subject and not in the atlas. In order to solve this limitation of the atlases, a new atlas-based segmentation method for pathological brains is proposed in this thesis as well as a validation method to assess this new approach. Results show that deep structures of the brain can still be efficiently segmented using an anatomic atlas even if they are largely deformed because of a lesion. The importance of including a priori knowledge is also presented in the application of brain tissue classification. The prior information represented by the tissue templates can be included in a brain tissue segmentation approach thanks to the registration techniques. This is another important issue presented in this thesis and it is analyzed through a comparative study of several non-supervised classification techniques. These methods are selected to represent the whole range of prior information that can be used in the classification process: the image intensity, the local spatial model, and the anatomical priors. Results show that the registration between the subject and the tissue templates allows the use of prior information but the accuracy of both the prior information and the registration highly influence the performance of the classification techniques. Another aim of this thesis is to present the concept of dynamic medical image analysis, in which the prior knowledge and the registration techniques are also of main importance. Actually, many medical image applications have the objective of statically analyzing one single image, as for instance in the case of atlas-based segmentation or brain tissue classification. But in other cases the implicit idea of changes detection is present. Intuitively, since the human body is changing continuously, we would like to do the image analysis from a dynamic point of view by detecting these changes, and by comparing them afterwards with templates to know if they are normal. The need of such approaches is even more evident in the case of many brain pathologies such as tumors, multiple sclerosis or degenerative diseases. In these cases, the key point is not only to detect but also to quantify and even characterize the evolving pathology. The evaluation of lesion variations over time can be very useful, for instance in the pharmaceutical research and clinical follow up. Of course, a sequence of images is needed in order to do such an analysis. Two approaches dealing with the idea of change detection are proposed as the last (but not least) issue presented in this work. The first one consists of performing a static analysis of each image forming the data set and, then, of comparing them. The second one consists of analyzing the non-rigid transformation between the sequence images instead of the images itself. Finally, both static and dynamic approaches are illustrated with a potential application: the cortical degeneration study is done using brain tissue segmentation, and the study of multiple sclerosis lesion evolution is performed by non-rigid deformation analysis. In conclusion, the importance of including a priori information encoded in the brain atlases in medical image analysis has been put in evidence with a wide range of possible applications. In the same way, the key role of registration techniques is shown not only as an efficient way to combine all the medical image modalities but also as a main element in the dynamic medical image analysis

Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction

Tuning the regularization hyperparameter $\alpha$ in inverse problems has been a longstanding problem. This is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of $\alpha$ to a given setting of interest in a quantitative manner. In this work, we propose a simulation-based approach to tune $\alpha$ for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal $\alpha$, chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting.Comment: 11 pages. This work has been submitted to MICCAI 202

Direct Fourier Tomographic Reconstruction Image-To-Image Filter

We present an open-source ITK implementation of a direct Fourier method for tomographic reconstruction, applicable to parallel-beam x-ray images. Direct Fourier reconstruction makes use of the central-slice theorem to build a polar 2D Fourier space from the 1D transformed projections of the scanned object, that is resampled into a Cartesian grid. Inverse 2D Fourier transform eventually yields the reconstructed image. Additionally, we provide a complex wrapper to the BSplineInterpolateImageFunction to overcome ITK’s current lack for image interpolators dealing with complex data types. A sample application is presented and extensively illustrated on the Shepp-Logan head phantom. We show that appropriate input zeropadding and 2D-DFT oversampling rates together with radial cubic b-spline interpolation improve 2D-DFT interpolation quality and are efficient remedies to reduce reconstruction artifacts

Exporting Contours to DICOM-RTSTRUCT

The “radiotherapy structure set” (RTSTRUCT) object of the DICOM standard is used for the transfer of patient structures and related data, between the devices found within and outside the radiotherapy department. It contains mainly the information for regions of interest (ROIs) and points of interest (e.g., dose reference points). In many cases, rather than manually drawing these ROIs on the CT images, one can indeed benefit from the wealth of automated segmentation algorithms available in ITK. But at present, it is not possible to export the ROIs obtained from ITK to RTSTRUCT format. In order to bridge this gap, we have developed a framework for exporting contour data to RTSTRUCT [1]

Exporting Contours to DICOM-RT Structure Set

This paper presents an ITK implementation for exporting the contours of the automated segmentation results to DICOM-RT Structure Set format. The “radiotherapy structure set” (RTSTRUCT) object of the DICOM standard is used for the transfer of patient structures and related data, between the devices found within and outside the radiotherapy department. It mainly contains the information of regions of interest (ROIs) and points of interest (E.g. dose reference points). In many cases, rather than manually drawing these ROIs on the CT images, one can indeed benefit from the automated segmentation algorithms already implemented in ITK. But at present, it is not possible to export the ROIs obtained from ITK to RTSTRUCT format. In order to bridge this gap, we have developed a framework for exporting contour data to RTSTRUCT. We provide here the complete implementation of RTSTRUCT exporter and present the details of the pipeline used. Results on a 3-D CT image of the Head and Neck (H&N) region are presented

Segmentation of the cortical plate in fetal brain MRI with a topological loss

The fetal cortical plate undergoes drastic morphological changes throughout early in utero development that can be observed using magnetic resonance (MR) imaging. An accurate MR image segmentation, and more importantly a topologically correct delineation of the cortical gray matter, is a key baseline to perform further quantitative analysis of brain development. In this paper, we propose for the first time the integration of a topological constraint, as an additional loss function, to enhance the morphological consistency of a deep learning-based segmentation of the fetal cortical plate. We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21 to 38 weeks of gestation, showing the significant benefits of our method through all gestational ages as compared to a baseline method. Furthermore, qualitative evaluation by three different experts on 130 randomly selected slices from 26 clinical MRIs evidences the out-performance of our method independently of the MR reconstruction quality.Comment: 4 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Segmentation of Fetal Pericerebral Spaces Based on Reconstructed High-Resolution MRI

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Bias Field Correction in Magnetic Resonance Images of a Rat Brain

Magnetic Resonance Imaging (MRI) is nowadays a widely used medical tool, as it is a non-invasive and non-harmful way to study inner soft tissues. One of the characteristics of this method is the bias field, also called Intensity In-homogeneity (IIH), which is an artifact that affects quantitative image analysis consisting in a low frequency variation of the brightness through all the image acquired. This undesired effect makes difficult medical functions such as visual inspection or also intensity-based segmentation [1]. The bias field is a deterministic function tied to complex physical interactions between the magnetic and electric fields and the living tissues, and this is why in this paper this bias field is directly corrected from the corrupt image data, using all the a priori information at our disposal. We will speciffically work on surface-coil images acquired at 9.4T, where the inhomogeneity effect is even stronger [2]

Atlas-free Brain Tissue Segmentation Using a Single T1-weighted MRI Acquisition

Many studies investigating the aging brain or disease-induced brain alterations rely on accurate and reproducible brain tissue segmentation. Being a preliminary processing step prior to the segmentation, reliableskull-stripping the removal ofnon-brain tissue is also crucial for all later image assessment. Typically, segmentation algorithms rely on an atlas i.e. pre-segmented template data. Brain morphology, however, differs considerably depending on age, sex and race. In addition, diseased brains may deviate significantly from the atlas information typically gained from healthy volunteers. The imposed prior atlas information can thus lead to degradation of segmentation results. The recently introduced MP2RAGE sequence provides a bias-free T1 contrast with heavily reduced T2*- and PD-weighting compared to the standard MP-RAGE [1]. To this end, it acquires two image volumes at different inversion times in one acquisition, combining them to a uniform, i.e. homogenous image. In this work, we exploit the advantageous contrast properties of the MP2RAGE and combine it with a Dixon (i.e. fat-water separation) approach. The information gained by the additional fat image of the head considerably improves the skull-stripping outcome [2]. In conjunction with the pure T1 contrast of the MP2RAGE uniform image, we achieve robust skull-stripping and brain tissue segmentation without the use of an atla