248 research outputs found
Two and three dimensional segmentation of multimodal imagery
The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes
Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems
FĂșze obrazu je dnes jednou z nejbÄĆŸnÄjĆĄĂch avĆĄak stĂĄle velmi diskutovanou oblastĂ v lĂ©kaĆskĂ©m zobrazovĂĄnĂ a hraje dĆŻleĆŸitou roli ve vĆĄech oblastech lĂ©kaĆskĂ© pĂ©Äe jako je diagnĂłza, lĂ©Äba a chirurgie. V tĂ©to dizertaÄnĂ prĂĄci jsou pĆedstaveny tĆi projekty, kterĂ© jsou velmi Ășzce spojeny s oblastĂ fĂșze medicĂnskĂœch dat. PrvnĂ projekt pojednĂĄvĂĄ o 3D CT subtrakÄnĂ angiografii dolnĂch konÄetin. V prĂĄci je vyuĆŸito kombinace kontrastnĂch a nekontrastnĂch dat pro zĂskĂĄnĂ kompletnĂho cĂ©vnĂho stromu. DruhĂœ projekt se zabĂœvĂĄ fĂșzĂ DTI a T1 vĂĄhovanĂœch MRI dat mozku. CĂlem tohoto projektu je zkombinovat stukturĂĄlnĂ a funkÄnĂ informace, kterĂ© umoĆŸĆujĂ zlepĆĄit znalosti konektivity v mozkovĂ© tkĂĄni. TĆetĂ projekt se zabĂœvĂĄ metastĂĄzemi v CT ÄasovĂœch datech pĂĄteĆe. Tento projekt je zamÄĆen na studium vĂœvoje metastĂĄz uvnitĆ obratlĆŻ ve fĂșzovanĂ© ÄasovĂ© ĆadÄ snĂmkĆŻ. Tato dizertaÄnĂ prĂĄce pĆedstavuje novou metodologii pro klasifikaci tÄchto metastĂĄz. VĆĄechny projekty zmĂnÄnĂ© v tĂ©to dizertaÄnĂ prĂĄci byly ĆeĆĄeny v rĂĄmci pracovnĂ skupiny zabĂœvajĂcĂ se analĂœzou lĂ©kaĆskĂœch dat, kterou vedl pan Prof. JiĆĂ Jan. Tato dizertaÄnĂ prĂĄce obsahuje registraÄnĂ ÄĂĄst prvnĂho a klasifikaÄnĂ ÄĂĄst tĆetĂho projektu. DruhĂœ projekt je pĆedstaven kompletnÄ. DalĆĄĂ ÄĂĄst prvnĂho a tĆetĂho projektu, obsahujĂcĂ specifickĂ© pĆedzpracovĂĄnĂ dat, jsou obsaĆŸeny v disertaÄnĂ prĂĄci mĂ©ho kolegy Ing. Romana Petera.Image fusion is one of todayÂŽs most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. JiĆĂ Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Unveiling healthcare data archiving: Exploring the role of artificial intelligence in medical image analysis
Gli archivi sanitari digitali possono essere considerati dei moderni database progettati per immagazzinare e gestire ingenti quantitaÌ di informazioni mediche, dalle cartelle cliniche dei pazienti, a studi clinici fino alle immagini mediche e a dati genomici. I dati strutturati e non strutturati che compongono gli archivi sanitari sono oggetto di scrupolose e rigorose procedure di validazione per garantire accuratezza, affidabilitaÌ e standardizzazione a fini clinici e di ricerca.
Nel contesto di un settore sanitario in continua e rapida evoluzione, lâintelligenza artificiale (IA) si propone come una forza trasformativa, capace di riformare gli archivi sanitari digitali migliorando la gestione, lâanalisi e il recupero di vasti set di dati clinici, al fine di ottenere decisioni cliniche piuÌ informate e ripetibili, interventi tempestivi e risultati migliorati per i pazienti.
Tra i diversi dati archiviati, la gestione e lâanalisi delle immagini mediche in archivi digitali presentano numerose sfide dovute allâeterogeneitaÌ dei dati, alla variabilitaÌ della qualitaÌ delle immagini, noncheÌ alla mancanza di annotazioni. Lâimpiego di soluzioni basate sullâIA puoÌ aiutare a risolvere efficacemente queste problematiche, migliorando lâaccuratezza dellâanalisi delle immagini, standardizzando la qualitaÌ dei dati e facilitando la generazione di annotazioni dettagliate.
Questa tesi ha lo scopo di utilizzare algoritmi di IA per lâanalisi di immagini mediche depositate in archivi sanitari digitali. Il presente lavoro propone di indagare varie tecniche di imaging medico, ognuna delle quali eÌ caratterizzata da uno specifico dominio di applicazione e presenta quindi un insieme unico di sfide, requisiti e potenziali esiti. In particolare, in questo lavoro di tesi saraÌ oggetto di approfondimento lâassistenza diagnostica degli algoritmi di IA per tre diverse tecniche di imaging, in specifici scenari clinici:
i) Immagini endoscopiche ottenute durante esami di laringoscopia; cioÌ include unâesplorazione approfondita di tecniche come la detection di keypoints per la stima della motilitaÌ delle corde vocali e la segmentazione di tumori del tratto aerodigestivo superiore;
ii) Immagini di risonanza magnetica per la segmentazione dei dischi intervertebrali, per la diagnosi e il trattamento di malattie spinali, cosiÌ come per lo svolgimento di interventi chirurgici guidati da immagini;
iii) Immagini ecografiche in ambito reumatologico, per la valutazione della sindrome del tunnel carpale attraverso la segmentazione del nervo mediano.
Le metodologie esposte in questo lavoro evidenziano lâefficacia degli algoritmi di IA nellâanalizzare immagini mediche archiviate. I progressi metodologici ottenuti sottolineano il notevole potenziale dellâIA nel rivelare informazioni implicitamente presenti negli archivi sanitari digitali
Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images
Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio
Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images
Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio
Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images
Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio
Segmentation and quantification of spinal cord gray matterâwhite matter structures in magnetic resonance images
This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers.
Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations.
We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences.
On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions.
From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background
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