An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio

Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current graphical processing units and the highly efficient implementation of such techniques. Hence, in this paper, we survey the key studies that are published between 2014 and 2020, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic-tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and further partitioned if the amount of works that fall under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites, containing masks of the aforementioned tissues, are thoroughly discussed, highlighting the organizers original contributions, and those of other researchers. Also, the metrics that are used excessively in literature are mentioned in our review stressing their relevancy to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing such as the scarcity of many studies on the vessels segmentation challenge, and why their absence needs to be dealt with in an accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver tissues segmentation based on automated ML-based technique

Coupled Shape Models for the Diagnosis of Organ Motion Restriction

Annähernd 30% der weltweiten Todesfälle sind auf Erkrankungen des Herzens und der Lunge zurückzuführen, wobei die meisten dieser Erkrankungen während ihres Verlaufs die Mobilität des betroffenen Organs verändern. Viele dieser To-desfälle könnten durch eine frühzeitige Erkennung und Behandlung der Erkran-kung vermieden werden. Deshalb wurden im Zuge dieser Arbeit Methoden ent-wickelt, um aus Segmentierungen von dynamischen Magnetresonanztomogra-phie-Daten quantitative Kennzahlen für die funktionale Analyse der Herz- und Lungenbewegung zu generieren. Ein automatisiertes Segmentierungsverfahren basierend auf gekoppelten Formmodellen wurde entwickelt, welches wechsel-seitige Informationen der Form und Geometrie mehrerer korrelierter Objekte mit einbezieht, und somit 40% bessere Ergebnisse im Vergleich zur Verwendung einzelner Modelle erzielte. Im Fall des Herzens wurde ein Volumenberechnungs-fehler von unter 13% erreicht, was in der Größenordnung der Interobserver-Variabilität liegt. Für die Lunge konnte ein Volumenfehler von unter 70ml gezeigt werden. Aus den Segmentierungsergebnissen wurden funktionale Parameter der lokalen Organdynamik abgeleitet und visualisiert, die gegen konventionelle Diag-nosemethoden evaluiert wurden und dabei gute Übereinstimmung zeigen, dar-über hinaus jedoch eine lokal und regionale Mobilitätscharakterisierung erlau-ben

Multimodality imaging of brown adipose tissue

There are two types of adipose tissue in the human body. White adipose tissue (WAT) stores energy, while brown adipose tissue (BAT) consumes it. BAT can be activated by exposure to cold to generate heat. Human adults seem to have recruitable ‘beige’ or ‘brite’ fat, which is derived from WAT. The cells can take on the appearance and function of BAT upon prolonged stimulation by cold, but the process can also be reversed. Thus adult human BAT contains a mixture of brown and white adipocytes at different stages, the triglyceride content being a continuous spectrum. Human BAT is highly insulin-sensitive. Decreases in BAT mass and activity may have a role in the development of obesity and diabetes in adulthood. The prevalence of these conditions is growing worldwide, leading to a global health issue and socioeconomic problem. This poses a great need for rapid and affordable means of studying fat tissue. The in vivo localization and activation state of BAT has been assessed by 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET/CT), which involves the intravenous injection of radioactive tracer, as well as exposure to radiation by computed tomography. Due to the differences in the tissue structure, water and iron content, magnetic resonance imaging (MRI) can reliably measure BAT volume and water content regardless of the activation state. As a method it is noninvasive, safe and more readily available than 18FDG-PET. The aim was to develop and test MRI methods for detecting, quantifying and examining BAT. The methods include in-phase and out-of-phase (in/opp) imaging, signal-fat-fraction (SFF) analysis based on the Dixon method, T2* relaxation time mapping and single-voxel proton magnetic resonance spectroscopy (1H MRS). Our results suggest that MRI methods can identify BAT and quantify fat deposit triglyceride content independent of cold-induced BAT activation and without radiation burden. It was also shown that the triglyceride content in supraclavicular fat deposits measured by 1H-MRS may be an independent marker of whole-body insulin sensitivity.Ruskean rasvakudoksen kuvantaminen eri kuvantamismenetelmillä Ihmiskehossa on kahdenlaista rasvakudosta. Valkoinen rasva (WAT) varastoi energiaa, ruskea rasva (BAT) kuluttaa sitä. Kylmäaltistus voi aktivoida ruskean rasvan tuottamaan lämpöä. Aikuisilla on hankinnaista "beige" tai "brite" rasvaa, joka on muuntunutta valkoista rasvaa. Pitkittyneessä kylmäaltistuksessa WAT-solut voivat ulkonäöltään ja toiminnaltaan muuntua BAT-solujen kaltaisiksi, mutta tapahtumasarja voi kulkea myös toiseen suuntaan. Aikuisella ihmisellä BAT on siis sekoitus eri asteisesti kypsyneitä ruskeita ja valkoisia rasvasoluja, joiden triglyseridipitoisuus muuttuu liukuvasti. Ihmisellä BAT on vahvasti insuliiniherkkää. BAT:n määrän ja aktiivisuuden vähentymisellä voi olla merkitystä lihavuudessa ja aikuisiän diabeteksessa. Näiden esiintyminen on globaalisti kasvussa, mikä johtaa maailmanlaajuisiin terveysongelmiin ja sosioekonomisiin ongelmiin. On siis tarvetta nopeille ja edullisille tavoille tutkia rasvakudosta. BAT:n sijaintia ja aktivoitumista on tutkittu 18F-fluorodeoksiglukoosipositroniemissio-tomografialla (18FDG-PET/CT), jossa aiheutuu sädealtistus suonensisäisestä radioaktiivisesta merkkiaineesta ja tietokonetomografiakuvauksesta. Kudoksen rakenne-eroista sekä vesi- ja rautapitoisuudesta johtuen magneettiresonanssikuvantaminen (MRI) mittaa luotettavasti BAT:n tilavuutta ja vesipitoisuutta. Menetelmänä se on kajoamaton, turvallinen ja helpommin saatavissa kuin PET. Tavoitteena oli kehittää ja testata MRI-menetelmiä ruskean rasvan toteamiseen, mittaamiseen ja arviointiin. Menetelminä olivat in-phase/out-of-phase (in/opp) -kuvantaminen, dixon-menetelmään perustuva signaalirasvasuhdekuvantaminen (SFF), T2*-relaksaatioaikakartoitus sekä yksittäisen vokselin protonimagneettispektroskopia (1H MRS). Tuloksiemme mukaan MRI-menetelmät pystyvät arvioimaan rasvan triglyseridipitoisuutta kylmäaktivaatiosta riippumatta ja ilman säderasitusta. Osoitimme myös, että solisluun alapuolisen BAT-kertymän triglyseridipitoisuus voi olla kehon insuliiniherkkyyden itsenäinen merkkitekijä

Improving foetal and neonatal echo-planar imaging with image-based shimming

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2015O Developing Human Connectome Project pretende realizar um progresso científico único através da criação do primeiro connectome 4D no início da vida do bebé. De forma a criar um mapa dinâmico da conectividade do cérebro do bebé, é fundamental obter imagens funcionais e com ponderação em difusão. A imagem eco-planar (EPI) é a principal sequência de ressonância magnética aplicada na aquisição dessa informação. Esta sequência permite uma aquisição rápida e repetida de imagens cerebrais permitindo mapear as flutuações da atividade cerebral (imagiologia funcional) bem como ter uma boa resolução nas imagens de difusão (movimento de moléculas de água no volume cerebral). No entanto, esta técnica está associada a artefactos de suscetibilidade. Estes artefactos surgem quando existem interfaces entre duas amostras com suscetibilidades magnéticas diferentes como sejam o tecido biológico e o ar. De forma a minimizar esses artefactos é necessário reduzir as heterogeneidades do campo magnético principal B0 através de shimming. O presente trabalho focou-se em shimming ativo, no qual o campo magnético é mapeado com base num modelo composto por funções harmónicas esféricas e são calculadas as correntes a aplicar às bobinas de shimming. Essas bobinas geram um campo magnético que compensa as heterogeneidades presentes anteriormente. Convencionalmente, as tentativas para superar este problema envolvem a utilização do método disponibilizado no sistema de ressonância magnética, nas quais o campo é mapeado com base em projecções (ex: FASTMAP). Este método é de execução rápida mas apresenta duas desvantagens principais: em primeiro lugar, o utilizador tem um controlo reduzido sobre o processo; em segundo, as regiões nas quais o campo é mapeado não são definidas com base na anatomia de interesse. No contexto deste trabalho, o controlo sobre o processo é importante no sentido de ser possível aplicar exatamente a mesma metodologia a um grupo elevado de sujeitos. Por seu lado, o mapeamento com base na anatomia permite obter uma optimização mais precisa. Com o surgimento de novas tecnologias passou a ser possível fazer um mapeamento mais detalhado do campo magnético com técnicas baseadas em imagem ao invés de projecções. Estas técnicas envolvem a definição de um volume relacionado com a anatomia, e que é incluído na totalidade na optimização do campo. O principal objetivo deste trabalho foi desenvolver uma ferramenta de shimming baseado em imagem a fim de otimizar o campo magnético no contexto de imagens de EPI do cérebro neonatal e fetal. O cérebro do bebé sofre alterações na sua dimensão e forma durante o seu desenvolvimento desde a idade fetal até neonatal. Em cada uma dessas fases o bebé encontra-se cercado por um ambiente diferente que requere uma abordagem específica ao mesmo. Neste sentido, o trabalho desenvolvido foi dividido em três partes principais: descrição da estrutura necessária para a correta aplicação do shimming, shimming neonatal e shimming fetal. Em primeiro lugar, as limitações do shimming baseado em imagem foram estudadas e o algoritmo básico para aplicar o método foi testado. Nesta parte do trabalho foi demonstrado que os campos gerados pelas bobinas de shim presentes no equipamento de ressonância magnética são consistentes com as funções harmónicas esféricas que compõem o modelo aplicado. O efeito do movimento da cama do equipamento sobre a eficiência do shimming foi também estudada. Foi possível corrigir a informação do sistema de coordenadas que descrevem o mapa de campo B0 de forma a incluir o movimento da cama necessário para a obtenção das imagens em sujeitos fetais. A segunda parte do trabalho focou-se no desenvolvimento do shimming para o caso neonatal. Foi desenvolvida uma ferramenta para definição de uma região de interesse, unwrapping da fase e cálculo das correntes de shim. Esta foi desenvolvida em ambiente MATLAB. Nos recém-nascidos o shimming deve ser aplicado numa região de interesse restrita ao cérebro devido à presença da interface ar/tecido no escalpe do bebé. Assim, a definição da região de interesse consistiu principalmente na aplicação de um limiar a fim de binarizar a imagem de magnitude, ajustada pelo utilizador. Em simultâneo foi implementada uma técnica de exclusão dos olhos com base na anatomia dos diferentes planos. No seu conjunto o método apresentou-se flexível de forma a ser ajustado ao sujeito em estudo. Quando aplicado com a mesma metodologia (limiar e exclusão de olhos) o volume incluído foi semelhante entre bebés. O método de shimming foi avaliado com base em três medidas de dispersão do mapa de campo residual: largura a meia altura, desvio padrão dos pixéis no interior da região de interesse e o intervalo de frequências no interior do qual 95 % dos pixéis se encontravam. A utilização de diferentes medidas permitiu a avaliação do m´etodo em relação a diferentes aspetos. Este método foi aplicado a 52 participantes recém-nascidos com idade gestacional média de 39.8 ± 1.7 semanas. O cálculo das correntes de shim permitiu gerar um campo magnético que melhorou a homogeneidade do campo B0 no volume adquirido, sendo consistente com o previsto. Uma imagem média do campo residual foi calculada mostrando que existem duas regiões (occipital e pequenas regiões laterais) nas quais o campo magnético B0 apresenta ainda heterogeneidades. Por fim, os resultados indicam que este método melhorou o campo perto da periferia do cérebro quando comparado com o método convencional disponibilizado no equipamento. O shimming neonatal (shimming ótimo ou OIBS) foi utilizado como alicerce para a implementação de um método ajustado às características das aquisições fetais. Existem duas características principais que devem ser tidas em conta. Em primeiro lugar, os fetos encontram-se envoltos em líquido amniótico e tecido materno pelo que o ambiente no qual estão inseridos permite que a região de interesse seja definida de forma menos restrita. Em segundo lugar, o facto de a cabeça do feto ser pequena pode levar à existência de valores de corrente das bobinas de shim elevados. Essas correntes, principalmente associadas às bobinas de segunda ordem geram campos de magnitude elevada na região do tecido adiposo, o que altera a sua frequência de ressonância. Desta forma, as técnicas de supressão de gordura específicas em frequência são menos efetivas e a imagem de EPI apresenta artefactos. Assim, a ferramenta para shimming fetal incluiu a definição de uma região de interesse cilíndrica e um método de shimming baseado em imagem com limites lineares (shimming limitado ou CIBS) impostos com base na frequência de ressonância do tecido adiposo. O CIBS consistiu na aplicação de limites superiores e inferiores ([-300 100] Hz) para a frequência dos pixéis pertencentes à gordura após a aplicação do shimming. Adicionalmente, o impulso de radiofrequência utilizado para a supressão de gordura foi estudado a fim o otimizar para a sua utilização no contexto do shimming fetal. Para o estudo dos parâmetros do impulso de radiofrequência, os rins de dois voluntários adultos saudáveis foram utilizados como simulação do ambiente fetal, devido as semelhanças entre a localização e interface entre tecidos. Os métodos OIBS e CIBS foram aplicados em 6 grávidas saudáveis com idades gestacional média de 28±6 semanas. Os mapas de campo residuais mostraram que as técnicas eram semelhantes em termos de homogeneidade no interior da região de interesse definida como cérebro, mas a segunda (CIBS) apresentou melhores resultados na supressão de gordura. Como estudo do impulso de radiofrequência foi demonstrado que o desvio do impulso em cerca de 100 Hz no sentido da frequência de ressonância da água melhoraria a supressão de gordura sem detrimento do sinal da água. A utilização do novo método CIBS em simultâneo com um impulso de radiofrequência otimizado mostrou ser a melhor solução para homogeneizar o campo e suprimir a gordura. Em conclusão, as ferramentas apresentadas permitiram melhorar a qualidade das imagens de EPI da cabeça do feto e do recém-nascido no contexto do Developing Human Connectome Project. O shimming neonatal mostrou ser um método consistente que pode facilmente ser utilizado por parte da equipa clínica. A nível fetal foi apresentado um método que demonstra a capacidade de superar as limitações demonstradas pelas técnicas convencionais.The Developing Human Connectome Project (dHCP) aims to make major scientific progress by creating the first 4-dimensional connectome of early life. Echo planar imaging (EPI) is the main acquisition technique applied in functional and diffusion imaging, which are central to map the human brain. This technique allows fast acquisition of spatial information enabling volumetric coverage of the whole brain, but it is associated with susceptibility artefacts. In order to minimize those artefacts it is necessary to reduce main magnetic field B0 in homogeneities through shimming. Conventionally, the attempts to overcome this problem use the manufacturer’s default method. Unfortunately, with those techniques the user has little control over the process, and the regions within which the field is corrected are not anatomically based. The main objective of this project was to develop an image-based shimming tool to optimize the magnetic field in the context of EPI images adjusted to the neonatal and foetal brains. The babies’ brain suffers changes in dimension and shape during its development from foetal to neonatal age. In each one of those stages the baby is surrounded by a different environment which requires a distinct shimming approach. As a result, the work was divided into three main parts: framework description, neonatal shimming and foetal shimming. First, the limitations of image-based shimming were investigated, and the framework to apply the method was described. It was demonstrated that fields generated by shim coils were consistent with the spherical harmonic model applied. In addition, the coordinate information of the B0 field map was corrected in order to include the table displacement needed for foetal imaging. Second, a tool was developed for neonatal shimming. The tool included region-of-interest (ROI) definition, phase unwrapping and shim calculation. The ROI definition implemented was flexible in order to adjust to each subject under study. When applying this approach while keeping the same threshold/eye exclusion methodology the volume included was similar between babies. The shim calculation allowed to generate shim values that improved homogeneity of the magnetic field within the volume imaged. This method slightly improved the field near the brain’s margins when compared with the manufacturer’s default techniques. Finally, for foetal shimming the groundwork of the neonatal tool was adjusted to this cohort characteristics. The tool for foetal shimming included additional cylindrical ROI definition and constrained image-based shimming. The constrained shimming allowed to account for the mother’s adipose tissue which in the presence of high shim values can lead to imperfect fat suppression. Along with the implementation of shimming tools, the radio frequency pulse used for fat suppression was studied. The new constrained image-based shimming showed similar results in terms of field homogeneity within the fetus’ brain when compared with the optimal image based shimming, with improvement of fat suppression that is enhanced when simultaneously used with the optimized fat suppression radiofrequency pulse

Motion robust acquisition and reconstruction of quantitative T2* maps in the developing brain

The goal of the research presented in this thesis was to develop methods for quantitative T2* mapping of the developing brain. Brain maturation in the early period of life involves complex structural and physiological changes caused by synaptogenesis, myelination and growth of cells. Molecular structures and biological processes give rise to varying levels of T2* relaxation time, which is an inherent contrast mechanism in magnetic resonance imaging. The knowledge of T2* relaxation times in the brain can thus help with evaluation of pathology by establishing its normative values in the key areas of the brain. T2* relaxation values are a valuable biomarker for myelin microstructure and iron concentration, as well as an important guide towards achievement of optimal fMRI contrast. However, fetal MR imaging is a significant step up from neonatal or adult MR imaging due to the complexity of the acquisition and reconstruction techniques that are required to provide high quality artifact-free images in the presence of maternal respiration and unpredictable fetal motion. The first contribution of this thesis, described in Chapter 4, presents a novel acquisition method for measurement of fetal brain T2* values. At the time of publication, this was the first study of fetal brain T2* values. Single shot multi-echo gradient echo EPI was proposed as a rapid method for measuring fetal T2* values by effectively freezing intra-slice motion. The study concluded that fetal T2* values are higher than those previously reported for pre-term neonates and decline with a consistent trend across gestational age. The data also suggested that longer than usual echo times or direct T2* measurement should be considered when performing fetal fMRI in order to reach optimal BOLD sensitivity. For the second contribution, described in Chapter 5, measurements were extended to a higher field strength of 3T and reported, for the first time, both for fetal and neonatal subjects at this field strength. The technical contribution of this work is a fully automatic segmentation framework that propagates brain tissue labels onto the acquired T2* maps without the need for manual intervention. The third contribution, described in Chapter 6, proposed a new method for performing 3D fetal brain reconstruction where the available data is sparse and is therefore limited in the use of current state of the art techniques for 3D brain reconstruction in the presence of motion. To enable a high resolution reconstruction, a generative adversarial network was trained to perform image to image translation between T2 weighted and T2* weighted data. Translated images could then be served as a prior for slice alignment and super resolution reconstruction of 3D brain image.Open Acces