Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

Detection of organs in CT images using Neural Networks

Táto práca sa zaoberá výskumom zobrazovacích metód v medicíne, klasických prístupov k segmentácii obrázkov, CT a konvolučným neuronovým sietiam. Praktickou časťou je implementácia architektúry 3D UNet pre segmentáciu chrbtice a jednotlivých stavcov z CT obrázkov a jej porovnanie s jej 2D verziou.This thesis contains research of the field of medical imaging, classical methods of image segmentation, computed tomography and convolutional neural networks. The practical part involves implementation of an architecture of 3D UNet for segmentation of the spine and specific vertebrae from CT scans. Furthermore, this architecture is compared to its 2D counterpart

Generative Models for Preprocessing of Hospital Brain Scans

I will in this thesis present novel computational methods for processing routine clinical brain scans. Such scans were originally acquired for qualitative assessment by trained radiologists, and present a number of difficulties for computational models, such as those within common neuroimaging analysis software. The overarching objective of this work is to enable efficient and fully automated analysis of large neuroimaging datasets, of the type currently present in many hospitals worldwide. The methods presented are based on probabilistic, generative models of the observed imaging data, and therefore rely on informative priors and realistic forward models. The first part of the thesis will present a model for image quality improvement, whose key component is a novel prior for multimodal datasets. I will demonstrate its effectiveness for super-resolving thick-sliced clinical MR scans and for denoising CT images and MR-based, multi-parametric mapping acquisitions. I will then show how the same prior can be used for within-subject, intermodal image registration, for more robustly registering large numbers of clinical scans. The second part of the thesis focusses on improved, automatic segmentation and spatial normalisation of routine clinical brain scans. I propose two extensions to a widely used segmentation technique. First, a method for this model to handle missing data, which allows me to predict entirely missing modalities from one, or a few, MR contrasts. Second, a principled way of combining the strengths of probabilistic, generative models with the unprecedented discriminative capability of deep learning. By introducing a convolutional neural network as a Markov random field prior, I can model nonlinear class interactions and learn these using backpropagation. I show that this model is robust to sequence and scanner variability. Finally, I show examples of fitting a population-level, generative model to various neuroimaging data, which can model, e.g., CT scans with haemorrhagic lesions

Innovative techniques to devise 3D-printed anatomical brain phantoms for morpho-functional medical imaging

Introduction. The Ph.D. thesis addresses the development of innovative techniques to create 3D-printed anatomical brain phantoms, which can be used for quantitative technical assessments on morpho-functional imaging devices, providing simulation accuracy not obtainable with currently available phantoms. 3D printing (3DP) technology is paving the way for advanced anatomical modelling in biomedical applications. Despite the potential already expressed by 3DP in this field, it is still little used for the realization of anthropomorphic phantoms of human organs with complex internal structures. Making an anthropomorphic phantom is very different from making a simple anatomical model and 3DP is still far from being plug-and-print. Hence, the need to develop ad-hoc techniques providing innovative solutions for the realization of anatomical phantoms with unique characteristics, and greater ease-of-use. Aim. The thesis explores the entire workflow (brain MRI images segmentation, 3D modelling and materialization) developed to prototype a new complex anthropomorphic brain phantom, which can simulate three brain compartments simultaneously: grey matter (GM), white matter (WM) and striatum (caudate nucleus and putamen, known to show a high uptake in nuclear medicine studies). The three separate chambers of the phantom will be filled with tissue-appropriate solutions characterized by different concentrations of radioisotope for PET/SPECT, para-/ferro-magnetic metals for MRI, and iodine for CT imaging. Methods. First, to design a 3D model of the brain phantom, it is necessary to segment MRI images and to extract an error-less STL (Standard Tessellation Language) description. Then, it is possible to materialize the prototype and test its functionality. - Image segmentation. Segmentation is one of the most critical steps in modelling. To this end, after demonstrating the proof-of-concept, a multi-parametric segmentation approach based on brain relaxometry was proposed. It includes a pre-processing step to estimate relaxation parameter maps (R1 = longitudinal relaxation rate, R2 = transverse relaxation rate, PD = proton density) from the signal intensities provided by MRI sequences of routine clinical protocols (3D-GrE T1-weighted, FLAIR and fast-T2-weighted sequences with ≤ 3 mm slice thickness). In the past, maps of R1, R2, and PD were obtained from Conventional Spin Echo (CSE) sequences, which are no longer suitable for clinical practice due to long acquisition times. Rehabilitating the multi-parametric segmentation based on relaxometry, the estimation of pseudo-relaxation maps allowed developing an innovative method for the simultaneous automatic segmentation of most of the brain structures (GM, WM, cerebrospinal fluid, thalamus, caudate nucleus, putamen, pallidus, nigra, red nucleus and dentate). This method allows the segmentation of higher resolution brain images for future brain phantom enhancements. - STL extraction. After segmentation, the 3D model of phantom is described in STL format, which represents the shapes through the approximation in manifold mesh (i.e., collection of triangles, which is continuous, without holes and with a positive – not zero – volume). For this purpose, we developed an automatic procedure to extract a single voxelized surface, tracing the anatomical interface between the phantom's compartments directly on the segmented images. Two tubes were designed for each compartment (one for filling and the other to facilitate the escape of air). The procedure automatically checks the continuity of the surface, ensuring that the 3D model could be exported in STL format, without errors, using a common image-to-STL conversion software. Threaded junctions were added to the phantom (for the hermetic closure) using a mesh processing software. The phantom's 3D model resulted correct and ready for 3DP. Prototyping. Finally, the most suitable 3DP technology is identified for the materialization. We investigated the material extrusion technology, named Fused Deposition Modeling (FDM), and the material jetting technology, named PolyJet. FDM resulted the best candidate for our purposes. It allowed materializing the phantom's hollow compartments in a single print, without having to print them in several parts to be reassembled later. FDM soluble internal support structures were completely removable after the materialization, unlike PolyJet supports. A critical aspect, which required a considerable effort to optimize the printing parameters, was the submillimetre thickness of the phantom walls, necessary to avoid distorting the imaging simulation. However, 3D printer manufacturers recommend maintaining a uniform wall thickness of at least 1 mm. The optimization of printing path made it possible to obtain strong, but not completely waterproof walls, approximately 0.5 mm thick. A sophisticated technique, based on the use of a polyvinyl-acetate solution, was developed to waterproof the internal and external phantom walls (necessary requirement for filling). A filling system was also designed to minimize the residual air bubbles, which could result in unwanted hypo-intensity (dark) areas in phantom-based imaging simulation. Discussions and conclusions. The phantom prototype was scanned trough CT and PET/CT to evaluate the realism of the brain simulation. None of the state-of-the-art brain phantoms allow such anatomical rendering of three brain compartments. Some represent only GM and WM, others only the striatum. Moreover, they typically have a poor anatomical yield, showing a reduced depth of the sulci and a not very faithful reproduction of the cerebral convolutions. The ability to simulate the three brain compartments simultaneously with greater accuracy, as well as the possibility of carrying out multimodality studies (PET/CT, PET/MRI), which represent the frontier of diagnostic imaging, give this device cutting-edge prospective characteristics. The effort to further customize 3DP technology for these applications is expected to increase significantly in the coming years

A Parameter-Efficient Deep Dense Residual Convolutional Neural Network for Volumetric Brain Tissue Segmentation from Magnetic Resonance Images

Brain tissue segmentation is a common medical image processing problem that deals with identifying a region of interest in the human brain from medical scans. It is a fundamental step towards neuroscience research and clinical diagnosis. Magnetic resonance (MR) images are widely used for segmentation in view of their non-invasive acquisition, and high spatial resolution and various contrast information. Accurate segmentation of brain tissues from MR images is very challenging due to the presence of motion artifacts, low signal-to-noise ratio, intensity overlaps, and intra- and inter-subject variability. Convolutional neural networks (CNNs) recently employed for segmentation provide remarkable advantages over the traditional and manual segmentation methods, however, their complex architectures and the large number of parameters make them computationally expensive and difficult to optimize. In this thesis, a novel learning-based algorithm using a three-dimensional deep convolutional neural network is proposed for efficient parameter reduction and compact feature representation to learn end-to-end mapping of T1-weighted (T1w) and/or T2-weighted (T2w) brain MR images to the probability scores of each voxel belonging to the different labels of brain tissues, namely, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) for segmentation. The basic idea in the proposed method is to use densely connected convolutional layers and residual skip-connections to increase representation capacity, facilitate better gradient flow, improve learning, and significantly reduce the number of parameters in the network. The network is independently trained on three different loss functions, cross-entropy, dice similarity, and a combination of the two and the results are compared with each other to investigate better loss function for the training. The model has the number of network parameters reduced by a significant amount compared to that of the state-of-the-art methods in brain tissue segmentation. Experiments are performed using the single-modality IBSR18 dataset containing high-resolution T1-weighted MR scans of diverse age groups, and the multi-modality iSeg-2017 dataset containing T1w and T2w MR scans of infants. It is shown that the proposed method provides the best performance on the test sets of both datasets amongst all the existing deep-learning based methods for brain tissue segmentation using the MR images and achieves competitive performance in the iSeg-2017 challenge with the number of parameters that is 47% to 98% lower than that of the other deep-learning based architectures

A Review on Brain Tumor Segmentation Based on Deep Learning Methods with Federated Learning Techniques

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues

Automatic segmentation of Nucleus Accumbens

Segmentation of subcortical structures in the brain has become an increasingly important topic in contemporary medicine. The ability to effi ciently isolate different regions of the human brain has allowed doctors and technicians to become more e fficient in the diagnosis of mental disorders and the evaluation of the patient conditions. An area of the brain whose possible segmentation has received particular attention is the Nucleus Accumbens, which is believed to play a central role in the reward circuit. In fact, studies of volumetric brain magnetic resonance imaging (MRI) have shown neuroanatomical abnormalities of this structure in adult attention defficit/hyperactivity disorder (ADHD), and speci cally a smaller average volume of the region. The use of a reliable automated segmentation method would therefore represent an extremely helpful and e fficient tool for identifying this disorder, especially when compared to manual volume labeling methods, which often turn out to be tedious and extremely time-consuming. However, automatic segmentation of the Accumbens is extremely di fficult to obtain, due to the lack of contrast with the surrounding structures. This means that most conventional segmentation methods are useless for this purpose, and makes the segmentation method selection a very delicate procedure. Consequently, the main objective of the thesis is the implementation of a robust algorithm for segmenting the Nucleus Accumbens structure. The research project aims to apply pre-existing segmentation methods to the Nucleus Accumbens, moving then to an evaluation of such methods and an estimation of how e ffective they are. Diff erent segmentation methods were used for this purpose; firstly, the standard Atlas Segmentation Approach was used, showing generally poor results paired with long computational times and high complexity. Moreover, this method has shown potential problems in the individuation of the correct region, leading, in some cases, to completely wrong segmentations. In addition to the fi rst method, Multi Atlas Segmentation and Adaptive Multi Atlas Segmentation methods have been implemented. The results have shown improved accuracy and better performance than the original method. Judging by the results, the segmentation of the Nucleus Accumbens has proven to be an extremely complicated task, both for the dimension of the structure itself and for the lack of contrast with the surrounding structures. In order to improve detection accuracy, combination of multiple methods is necessary, as using a single method for the segmentation process can lead to an incorrect labeling