Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

腹部CT像上の複数オブジェクトのセグメンテーションのための統計的手法に関する研究

Computer aided diagnosis (CAD) is the use of a computer-generated output as an auxiliary tool for the assistance of efficient interpretation and accurate diagnosis. Medical image segmentation has an essential role in CAD in clinical applications. Generally, the task of medical image segmentation involves multiple objects, such as organs or diffused tumor regions. Moreover, it is very unfavorable to segment these regions from abdominal Computed Tomography (CT) images because of the overlap in intensity and variability in position and shape of soft tissues. In this thesis, a progressive segmentation framework is proposed to extract liver and tumor regions from CT images more efficiently, which includes the steps of multiple organs coarse segmentation, fine segmentation, and liver tumors segmentation. Benefit from the previous knowledge of the shape and its deformation, the Statistical shape model (SSM) method is firstly utilized to segment multiple organs regions robustly. In the process of building an SSM, the correspondence of landmarks is crucial to the quality of the model. To generate a more representative prototype of organ surface, a k-mean clustering method is proposed. The quality of the SSMs, which is measured by generalization ability, specificity, and compactness, was improved. We furtherly extend the shapes correspondence to multiple objects. A non-rigid iterative closest point surface registration process is proposed to seek more properly corresponded landmarks across the multi-organ surfaces. The accuracy of surface registration was improved as well as the model quality. Moreover, to localize the abdominal organs simultaneously, we proposed a random forest regressor cooperating intensity features to predict the position of multiple organs in the CT image. The regions of the organs are substantially restrained using the trained shape models. The accuracy of coarse segmentation using SSMs was increased by the initial information of organ positions.Consequently, a pixel-wise segmentation using the classification of supervoxels is applied for the fine segmentation of multiple organs. The intensity and spatial features are extracted from each supervoxels and classified by a trained random forest. The boundary of the supervoxels is closer to the real organs than the previous coarse segmentation. Finally, we developed a hybrid framework for liver tumor segmentation in multiphase images. To deal with these issues of distinguishing and delineating tumor regions and peripheral tissues, this task is accomplished in two steps: a cascade region-based convolutional neural network (R-CNN) with a refined head is trained to locate the bounding boxes that contain tumors, and a phase-sensitive noise filtering is introduced to refine the following segmentation of tumor regions conducted by a level-set-based framework. The results of tumor detection show the adjacent tumors are successfully separated by the improved cascaded R-CNN. The accuracy of tumor segmentation is also improved by our proposed method. 26 cases of multi-phase CT images were used to validate our proposed method for the segmentation of liver tumors. The average precision and recall rates for tumor detection are 76.8% and 84.4%, respectively. The intersection over union, true positive rate, and false positive rate for tumor segmentation are 72.7%, 76.2%, and 4.75%, respectively.九州工業大学博士学位論文 学位記番号: 工博甲第546号 学位授与年月日: 令和4年3月25日1 Introduction|2 Literature Review|3 Statistical Shape Model Building|4 Multi-organ Segmentation|5 Liver Tumors Segmentation|6 Summary and Outlook九州工業大学令和3年

Medical image registration using unsupervised deep neural network: A scoping literature review

In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehensive scoping review on the state-of-the-art literature of medical image registration studies based on unsupervised deep neural networks is conducted, encompassing all the related studies published in this field to this date. Here, we have tried to summarize the latest developments and applications of unsupervised deep learning-based registration methods in the medical field. Fundamental and main concepts, techniques, statistical analysis from different viewpoints, novelties, and future directions are elaborately discussed and conveyed in the current comprehensive scoping review. Besides, this review hopes to help those active readers, who are riveted by this field, achieve deep insight into this exciting field

Navigation system based in motion tracking sensor for percutaneous renal access

Tese de Doutoramento em Engenharia BiomédicaMinimally-invasive kidney interventions are daily performed to diagnose and treat several renal diseases. Percutaneous renal access (PRA) is an essential but challenging stage for most of these procedures, since its outcome is directly linked to the physician’s ability to precisely visualize and reach the anatomical target. Nowadays, PRA is always guided with medical imaging assistance, most frequently using X-ray based imaging (e.g. fluoroscopy). Thus, radiation on the surgical theater represents a major risk to the medical team, where its exclusion from PRA has a direct impact diminishing the dose exposure on both patients and physicians. To solve the referred problems this thesis aims to develop a new hardware/software framework to intuitively and safely guide the surgeon during PRA planning and puncturing. In terms of surgical planning, a set of methodologies were developed to increase the certainty of reaching a specific target inside the kidney. The most relevant abdominal structures for PRA were automatically clustered into different 3D volumes. For that, primitive volumes were merged as a local optimization problem using the minimum description length principle and image statistical properties. A multi-volume Ray Cast method was then used to highlight each segmented volume. Results show that it is possible to detect all abdominal structures surrounding the kidney, with the ability to correctly estimate a virtual trajectory. Concerning the percutaneous puncturing stage, either an electromagnetic or optical solution were developed and tested in multiple in vitro, in vivo and ex vivo trials. The optical tracking solution aids in establishing the desired puncture site and choosing the best virtual puncture trajectory. However, this system required a line of sight to different optical markers placed at the needle base, limiting the accuracy when tracking inside the human body. Results show that the needle tip can deflect from its initial straight line trajectory with an error higher than 3 mm. Moreover, a complex registration procedure and initial setup is needed. On the other hand, a real-time electromagnetic tracking was developed. Hereto, a catheter was inserted trans-urethrally towards the renal target. This catheter has a position and orientation electromagnetic sensor on its tip that function as a real-time target locator. Then, a needle integrating a similar sensor is used. From the data provided by both sensors, one computes a virtual puncture trajectory, which is displayed in a 3D visualization software. In vivo tests showed a median renal and ureteral puncture times of 19 and 51 seconds, respectively (range 14 to 45 and 45 to 67 seconds). Such results represent a puncture time improvement between 75% and 85% when comparing to state of the art methods. 3D sound and vibrotactile feedback were also developed to provide additional information about the needle orientation. By using these kind of feedback, it was verified that the surgeon tends to follow a virtual puncture trajectory with a reduced amount of deviations from the ideal trajectory, being able to anticipate any movement even without looking to a monitor. Best results show that 3D sound sources were correctly identified 79.2 ± 8.1% of times with an average angulation error of 10.4º degrees. Vibration sources were accurately identified 91.1 ± 3.6% of times with an average angulation error of 8.0º degrees. Additionally to the EMT framework, three circular ultrasound transducers were built with a needle working channel. One explored different manufacture fabrication setups in terms of the piezoelectric materials, transducer construction, single vs. multi array configurations, backing and matching material design. The A-scan signals retrieved from each transducer were filtered and processed to automatically detect reflected echoes and to alert the surgeon when undesirable anatomical structures are in between the puncture path. The transducers were mapped in a water tank and tested in a study involving 45 phantoms. Results showed that the beam cross-sectional area oscillates around the ceramics radius and it was possible to automatically detect echo signals in phantoms with length higher than 80 mm. Hereupon, it is expected that the introduction of the proposed system on the PRA procedure, will allow to guide the surgeon through the optimal path towards the precise kidney target, increasing surgeon’s confidence and reducing complications (e.g. organ perforation) during PRA. Moreover, the developed framework has the potential to make the PRA free of radiation for both patient and surgeon and to broad the use of PRA to less specialized surgeons.Intervenções renais minimamente invasivas são realizadas diariamente para o tratamento e diagnóstico de várias doenças renais. O acesso renal percutâneo (ARP) é uma etapa essencial e desafiante na maior parte destes procedimentos. O seu resultado encontra-se diretamente relacionado com a capacidade do cirurgião visualizar e atingir com precisão o alvo anatómico. Hoje em dia, o ARP é sempre guiado com recurso a sistemas imagiológicos, na maior parte das vezes baseados em raios-X (p.e. a fluoroscopia). A radiação destes sistemas nas salas cirúrgicas representa um grande risco para a equipa médica, aonde a sua remoção levará a um impacto direto na diminuição da dose exposta aos pacientes e cirurgiões. De modo a resolver os problemas existentes, esta tese tem como objetivo o desenvolvimento de uma framework de hardware/software que permita, de forma intuitiva e segura, guiar o cirurgião durante o planeamento e punção do ARP. Em termos de planeamento, foi desenvolvido um conjunto de metodologias de modo a aumentar a eficácia com que o alvo anatómico é alcançado. As estruturas abdominais mais relevantes para o procedimento de ARP, foram automaticamente agrupadas em volumes 3D, através de um problema de optimização global com base no princípio de “minimum description length” e propriedades estatísticas da imagem. Por fim, um procedimento de Ray Cast, com múltiplas funções de transferência, foi utilizado para enfatizar as estruturas segmentadas. Os resultados mostram que é possível detetar todas as estruturas abdominais envolventes ao rim, com a capacidade para estimar corretamente uma trajetória virtual. No que diz respeito à fase de punção percutânea, foram testadas duas soluções de deteção de movimento (ótica e eletromagnética) em múltiplos ensaios in vitro, in vivo e ex vivo. A solução baseada em sensores óticos ajudou no cálculo do melhor ponto de punção e na definição da melhor trajetória a seguir. Contudo, este sistema necessita de uma linha de visão com diferentes marcadores óticos acoplados à base da agulha, limitando a precisão com que a agulha é detetada no interior do corpo humano. Os resultados indicam que a agulha pode sofrer deflexões à medida que vai sendo inserida, com erros superiores a 3 mm. Por outro lado, foi desenvolvida e testada uma solução com base em sensores eletromagnéticos. Para tal, um cateter que integra um sensor de posição e orientação na sua ponta, foi colocado por via trans-uretral junto do alvo renal. De seguida, uma agulha, integrando um sensor semelhante, é utilizada para a punção percutânea. A partir da diferença espacial de ambos os sensores, é possível gerar uma trajetória de punção virtual. A mediana do tempo necessário para puncionar o rim e ureter, segundo esta trajetória, foi de 19 e 51 segundos, respetivamente (variações de 14 a 45 e 45 a 67 segundos). Estes resultados representam uma melhoria do tempo de punção entre 75% e 85%, quando comparados com o estado da arte dos métodos atuais. Além do feedback visual, som 3D e feedback vibratório foram explorados de modo a fornecer informações complementares da posição da agulha. Verificou-se que com este tipo de feedback, o cirurgião tende a seguir uma trajetória de punção com desvios mínimos, sendo igualmente capaz de antecipar qualquer movimento, mesmo sem olhar para o monitor. Fontes de som e vibração podem ser corretamente detetadas em 79,2 ± 8,1% e 91,1 ± 3,6%, com erros médios de angulação de 10.4º e 8.0 graus, respetivamente. Adicionalmente ao sistema de navegação, foram também produzidos três transdutores de ultrassom circulares com um canal de trabalho para a agulha. Para tal, foram exploradas diferentes configurações de fabricação em termos de materiais piezoelétricos, transdutores multi-array ou singulares e espessura/material de layers de suporte. Os sinais originados em cada transdutor foram filtrados e processados de modo a detetar de forma automática os ecos refletidos, e assim, alertar o cirurgião quando existem variações anatómicas ao longo do caminho de punção. Os transdutores foram mapeados num tanque de água e testados em 45 phantoms. Os resultados mostraram que o feixe de área em corte transversal oscila em torno do raio de cerâmica, e que os ecos refletidos são detetados em phantoms com comprimentos superiores a 80 mm. Desta forma, é expectável que a introdução deste novo sistema a nível do ARP permitirá conduzir o cirurgião ao longo do caminho de punção ideal, aumentado a confiança do cirurgião e reduzindo possíveis complicações (p.e. a perfuração dos órgãos). Além disso, de realçar que este sistema apresenta o potencial de tornar o ARP livre de radiação e alarga-lo a cirurgiões menos especializados.The present work was only possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant with reference SFRH/BD/74276/2010 funded by FCT/MEC (PIDDAC) and by Fundo Europeu de Desenvolvimento Regional (FEDER), Programa COMPETE - Programa Operacional Factores de Competitividade (POFC) do QREN

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based model for biliary tree initial diagnosis. To segment the biliary tree from the Magnetic Resonance Image, the framework used different image processing approaches (MRI). After the image’s region of interest was segmented, numerous calculations were performed on it to extract 10 features, including major and minor axes, bile duct area, biliary tree area, compactness, and some textural features (contrast, mean, variance and correlation). This study used a database of images from King Hussein Medical Center in Amman, Jordan, which included 200 MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After the characteristics are extracted, various classifiers are used to determine the patients’ condition in terms of their health (normal or dilated). The findings demonstrate that the extracted features perform well with all classifiers in terms of accuracy and area under the curve. This study is unique in that it uses an automated approach to segment the biliary tree from MRI images, as well as scientifically correlating retrieved features with biliary tree status that has never been done before in the literature

Machine learning-based automated segmentation with a feedback loop for 3D synchrotron micro-CT

Die Entwicklung von Synchrotronlichtquellen der dritten Generation hat die Grundlage für die Untersuchung der 3D-Struktur opaker Proben mit einer Auflösung im Mikrometerbereich und höher geschaffen. Dies führte zur Entwicklung der Röntgen-Synchrotron-Mikro-Computertomographie, welche die Schaffung von Bildgebungseinrichtungen zur Untersuchung von Proben verschiedenster Art förderte, z.B. von Modellorganismen, um die Physiologie komplexer lebender Systeme besser zu verstehen. Die Entwicklung moderner Steuerungssysteme und Robotik ermöglichte die vollständige Automatisierung der Röntgenbildgebungsexperimente und die Kalibrierung der Parameter des Versuchsaufbaus während des Betriebs. Die Weiterentwicklung der digitalen Detektorsysteme führte zu Verbesserungen der Auflösung, des Dynamikbereichs, der Empfindlichkeit und anderer wesentlicher Eigenschaften. Diese Verbesserungen führten zu einer beträchtlichen Steigerung des Durchsatzes des Bildgebungsprozesses, aber auf der anderen Seite begannen die Experimente eine wesentlich größere Datenmenge von bis zu Dutzenden von Terabyte zu generieren, welche anschließend manuell verarbeitet wurden. Somit ebneten diese technischen Fortschritte den Weg für die Durchführung effizienterer Hochdurchsatzexperimente zur Untersuchung einer großen Anzahl von Proben, welche Datensätze von besserer Qualität produzierten. In der wissenschaftlichen Gemeinschaft besteht daher ein hoher Bedarf an einem effizienten, automatisierten Workflow für die Röntgendatenanalyse, welcher eine solche Datenlast bewältigen und wertvolle Erkenntnisse für die Fachexperten liefern kann. Die bestehenden Lösungen für einen solchen Workflow sind nicht direkt auf Hochdurchsatzexperimente anwendbar, da sie für Ad-hoc-Szenarien im Bereich der medizinischen Bildgebung entwickelt wurden. Daher sind sie nicht für Hochdurchsatzdatenströme optimiert und auch nicht in der Lage, die hierarchische Beschaffenheit von Proben zu nutzen. Die wichtigsten Beiträge der vorliegenden Arbeit sind ein neuer automatisierter Analyse-Workflow, der für die effiziente Verarbeitung heterogener Röntgendatensätze hierarchischer Natur geeignet ist. Der entwickelte Workflow basiert auf verbesserten Methoden zur Datenvorverarbeitung, Registrierung, Lokalisierung und Segmentierung. Jede Phase eines Arbeitsablaufs, die eine Trainingsphase beinhaltet, kann automatisch feinabgestimmt werden, um die besten Hyperparameter für den spezifischen Datensatz zu finden. Für die Analyse von Faserstrukturen in Proben wurde eine neue, hochgradig parallelisierbare 3D-Orientierungsanalysemethode entwickelt, die auf einem neuartigen Konzept der emittierenden Strahlen basiert und eine präzisere morphologische Analyse ermöglicht. Alle entwickelten Methoden wurden gründlich an synthetischen Datensätzen validiert, um ihre Anwendbarkeit unter verschiedenen Abbildungsbedingungen quantitativ zu bewerten. Es wurde gezeigt, dass der Workflow in der Lage ist, eine Reihe von Datensätzen ähnlicher Art zu verarbeiten. Darüber hinaus werden die effizienten CPU/GPU-Implementierungen des entwickelten Workflows und der Methoden vorgestellt und der Gemeinschaft als Module für die Sprache Python zur Verfügung gestellt. Der entwickelte automatisierte Analyse-Workflow wurde erfolgreich für Mikro-CT-Datensätze angewandt, die in Hochdurchsatzröntgenexperimenten im Bereich der Entwicklungsbiologie und Materialwissenschaft gewonnen wurden. Insbesondere wurde dieser Arbeitsablauf für die Analyse der Medaka-Fisch-Datensätze angewandt, was eine automatisierte Segmentierung und anschließende morphologische Analyse von Gehirn, Leber, Kopfnephronen und Herz ermöglichte. Darüber hinaus wurde die entwickelte Methode der 3D-Orientierungsanalyse bei der morphologischen Analyse von Polymergerüst-Datensätzen eingesetzt, um einen Herstellungsprozess in Richtung wünschenswerter Eigenschaften zu lenken

Segmentation, tracking, and kinematics of lung parenchyma and lung tumors from 4D CT with application to radiation treatment planning.

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of the lung tissue including Jacobian and strain at high resolutions and as a function of time. Radiation Treatment of patients with lung cancer can benefit from knowledge of these measures of regional ventilation. Graph-cuts techniques have been popular for image segmentation since they are able to treat highly textured data via robust global optimization, avoiding local minima in graph based optimization. The graph-cuts methods have been used to extract globally optimal boundaries from images by s/t cut, with energy function based on model-specific visual cues, and useful topological constraints. The method makes N-dimensional globally optimal segmentation possible with good computational efficiency. Even though the graph-cuts method can extract objects where there is a clear intensity difference, segmentation of organs or tumors pose a challenge. For organ segmentation, many segmentation methods using a shape prior have been proposed. However, in the case of lung tumors, the shape varies from patient to patient, and with location. In this thesis, we use a shape prior for tumors through a training step and PCA analysis based on the Active Shape Model (ASM). The method has been tested on real patient data from the Brown Cancer Center at the University of Louisville. We performed temporal B-spline deformable registration of the 4-D CT data - this yielded 3-D deformation fields between successive respiratory phases from which measures of regional lung function were determined. During the respiratory cycle, the lung volume changes and five different lobes of the lung (two in the left and three in the right lung) show different deformation yielding different strain and Jacobian maps. In this thesis, we determine the regional lung mechanics in the Lagrangian frame of reference through different respiratory phases, for example, Phase10 to 20, Phase10 to 30, Phase10 to 40, and Phase10 to 50. Single photon emission computed tomography (SPECT) lung imaging using radioactive tracers with SPECT ventilation and SPECT perfusion imaging also provides functional information. As part of an IRB-approved study therefore, we registered the max-inhale CT volume to both VSPECT and QSPECT data sets using the Demon\u27s non-rigid registration algorithm in patient subjects. Subsequently, statistical correlation between CT ventilation images (Jacobian and strain values), with both VSPECT and QSPECT was undertaken. Through statistical analysis with the Spearman\u27s rank correlation coefficient, we found that Jacobian values have the highest correlation with both VSPECT and QSPECT