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A Novel Approach for the Visualisation and Progression Tracking of Metastatic Bone Disease
Metastatic bone disease (MBD) is a common secondary feature of cancer that can cause significant complications, including severe pain and death. Current methods of diagnosis require a highly trained radiologist capable of interpreting medical images and recognising the sites of MBD. These medical images are often noisy, two dimensional, greyscale and usually have a poor resolution.
In order to help assist with these issues, several studies have shown that computer aided methods can locate MBD within medical images. However these methods are limited in scope, accuracy, sensitivity, explainability and do not improve upon the poor visualisations of the underlying medical imaging data.
To address these limitations, I have developed a novel method of automatic MBD assessment and visualisation using computed tomography (CT) imaging data as the input. The method is fully automated and does not require any human interaction -- although users can interact with a viewer that visualises the results. This method has been tested on CT data from prostate cancer patients as prostate cancer is one of the most common sources of MBD.
The method described in this thesis has a sensitivity of 0.871 when detecting sclerotic and lytic lesions within a single data set. This sensitivity is comparable to existing methods, however the scope in detecting these lesions was limited to the vertebrae in previous studies. My method significantly expands this scope to include the ribs, vertebrae, pelvis and proximal femurs.
The work in this thesis also provides novel visualisations of the disease and does not suffer from explainability issues that plague modern machine learning algorithms.
In addition, I developed a novel method of tracking the spread of MBD at multiple time points using longitudinal CT data. This method is capable of calculating the change in lesion volume size across multiple time points, providing a novel numerical assessment.The Armstrong Trus
Fusion and Analysis of Multidimensional Medical Image Data
AnalĂœza medicĂnskĂœch obrazĆŻ je pĆedmÄtem zĂĄkladnĂho vĂœzkumu jiĆŸ Ćadu let. Za tu dobu bylo v tĂ©to oblasti publikovĂĄno mnoho vĂœzkumnĂœch pracĂ zabĂœvajĂcĂch se dĂlÄĂmi ÄĂĄstmi jako je rekonstrukce obrazĆŻ, restaurace, segmentace, klasifikace, registrace (lĂcovĂĄnĂ) a fĂșze. KromÄ obecnĂ©ho Ășvodu, pojednĂĄvĂĄ tato disertaÄnĂ prĂĄce o dvou medicĂnsky orientovanĂœch tĂ©matech, jeĆŸ byla formulovĂĄna ve spoluprĂĄci s Philips Netherland BV, divizĂ Philips Healthcare. PrvnĂ tĂ©ma je zamÄĆeno na oblast zpracovĂĄnĂ obrazĆŻ subtrakÄnĂ angiografie dolnĂch konÄetin ÄlovÄka zĂskanĂœch pomocĂ vĂœpoÄetnĂ X-Ray tomografie (CT). SubtrakÄnĂ angiografie je obvykle vyuĆŸĂvanĂĄ pĆi podezĆenĂ na perifernĂ cĂ©vnĂ onemocnÄnĂ (PAOD) nebo pĆi akutnĂm poĆĄkozenĂ dolnĂch konÄetin jako jsou fraktury apod. SouÄasnĂ© komerÄnĂ metody nejsou dostateÄnÄ spolehlivĂ© uĆŸ v pĆedzpracovĂĄnĂ, jako je napĆĂklad odstranÄnĂ pacientskĂ©ho stolu, pokrĂœvky, dlahy, apod. Spolehlivost a pĆesnost identifikace cĂ©v v subtrahovanĂœch datech vedoucĂch v blĂzkosti kostĂ je v dĆŻsledku Partial Volume artefaktu rovnÄĆŸ nĂzkĂĄ. AutomatickĂ© odstranÄnĂ kalcifikacĂ nebo detekce malĂœch cĂ©v doplĆujĂcĂch nezbytnou informaci o nĂĄhradnĂm zĂĄsobenĂ dolnĂch konÄetin krvĂ v pĆĂpadÄ pĆeruĆĄenĂ hlavnĂch zĂĄsobujĂcĂch cĂ©v v souÄasnĂ© dobÄ rovnÄĆŸ nesplĆujĂ kritĂ©ria pro plnÄ automatickĂ© zpracovĂĄnĂ. Proto hlavnĂm cĂlem tĂœkajĂcĂ se tohoto tĂ©matu bylo vyvinout automatickĂœ systĂ©m, kterĂœ by mohl souÄasnĂ© nedostatky v CTSA vyĆĄetĆenĂ odstranit. DruhĂ© tĂ©ma je orientovĂĄno na identifikaci patologickĂœch zmÄn na pĂĄteĆi ÄlovÄka v CT obrazech se zamÄĆenĂm na osteolytickĂ© a osteoblastickĂ© lĂ©ze u jednotlivĂœch obratlĆŻ. Tyto zmÄny obvykle nastĂĄvajĂ v dĆŻsledkĆŻ postiĆŸenĂ metastazujĂcĂm procesem rakovinovĂ©ho onemocnÄnĂ. Pro detekci patologickĂœch zmÄn je pak potĆeba identifikace a segmentace jednotlivĂœch obratlĆŻ. PĆesnost analĂœzy jednotlivĂœch lĂ©zĂ vĆĄak zĂĄvisĂ rovnÄĆŸ na sprĂĄvnĂ© identifikaci tÄla a zadnĂch segmentĆŻ u jednotlivĂœch obratlĆŻ a na segmentaci trabekulĂĄrnĂho centra obratlĆŻ, tj. odstranÄnĂ kortikĂĄlnĂ kosti. BÄhem lĂ©Äby mohou bĂœt pacienti skenovĂĄni vĂcekrĂĄt, obvykle s nÄkolika-mesĂÄnĂm odstupem. HodnocenĂ pĆĂpadnĂ©ho vĂœvoje jiĆŸ detekovanĂœch patologickĂœch zmÄn pak logicky vychĂĄzĂ ze sprĂĄvnĂ© detekce patologiĂ v jednotlivĂœch obratlech korespondujĂcĂch si v jednotlivĂœch akvizicĂch. JelikoĆŸ jsou pĆĂsluĆĄnĂ© obratle v jednotlivĂœch akvizicĂch obvykle na rĆŻznĂ© pozici, jejich fĂșze, vedoucĂ k analĂœze ÄasovĂ©ho vĂœvoje detekovanĂœch patologiĂ, je komplikovanĂĄ. PoĆŸadovanĂœm vĂœsledkem v tomto tĂ©matu je vytvoĆenĂ komplexnĂho systĂ©mu pro detekci patologickĂœch zmÄn v pĂĄteĆi, pĆedevĆĄĂm osteoblastickĂœch a osteolytickĂœch lĂ©zĂ. TakovĂœ systĂ©m tedy musĂ umoĆŸnovat jak segmentaci jednotlivĂœch obratlĆŻ, jejich automatickĂ© rozdÄlenĂ na hlavnĂ ÄĂĄsti a odstranÄnĂ kortikĂĄlnĂ kosti, tak takĂ© detekci patologickĂœch zmÄn a jejich hodnocenĂ. AÄkoliv je tato disertaÄnĂ prĂĄce v obou vĂœĆĄe zmĂnÄnĂœch tĂ©matech primĂĄrnÄ zamÄĆena na experimentĂĄlnĂ ÄĂĄst zpracovĂĄnĂ medicĂnskĂœch obrazĆŻ, zabĂœvĂĄ se vĆĄemi nezbytnĂœmi kroky, jako je pĆedzpracovĂĄnĂ, registrace, dodateÄnĂ© zpracovĂĄnĂ a hodnocenĂ vĂœsledkĆŻ, vedoucĂmi k moĆŸnĂ© aplikovatelnosti obou systĂ©mu v klinickĂ© praxi. JelikoĆŸ oba systĂ©my byly ĆeĆĄeny v rĂĄmci tĂœmovĂ© spoluprĂĄce jako celek, u obou tĂ©mat jsou pro nÄkterĂ© konkrĂ©tnĂ kroky uvedeny odkazy na doktorskou prĂĄci MiloĆĄe MalĂnskĂ©ho.Analysis of medical images has been subject of basic research for many years. Many research papers have been published in the field related to image analysis and focused on partial aspects such as reconstruction, restoration, segmentation and classification, registration (spatial alignment) and fusion. Besides the introduction of related general concepts used in medical image processing, this thesis deals with two specific medical problems formulated in cooperation with Philips Netherland BV, Philips Healthcare division. The first topic is focused on subtraction angiography in patientsâ lower legs utilizing image data from X-Ray computed tomography (CT). CT subtraction angiography (CTSA) is typically used for indication of the Peripheral Artery Occlusive Disease (PAOD) and for examination of acute injuries of lower legs such as acute fractures, etc. Current methods in clinical praxis are not sufficient regarding the pre-processing such as masking of patient desk, cover, splint, etc. The subtraction of blood vessels adjacent to neighboring bones in lower legs is of low accuracy due to the Partial Volume artifact. Masking of calcifications and detection of tiny blood vessels complementing necessary information about the alternative blood supply in lower legs in case of obstruction in main arteries is also not reliable for fully automated process presently. Therefore, the main aim regarding this topic was to develop an automated framework that could overcome current shortcomings in CTSA examination. The second topic is oriented on the identification and evaluation of pathologic changes in human spine, focusing on osteolytic and osteoblastic lesions in individual vertebrae in CT images. Such changes occur typically as a consequence of metastasizing process of cancerous disease. For the detection of pathologic changes, an identification and segmentation of individual vertebrae is necessary. Moreover, the analysis of individual lesions in vertebrae depends also on correct identification of vertebral body and posterior segments of each vertebra, and on segmentation of their trabecular centers. Patients are typically examined more than once during their therapy. Then, the evaluation of possible tumorous progression is based on accurate detection of pathologies in individual vertebrae in the base-line and corresponding follow-up images. Since the corresponding vertebrae are in mutually different positions in the follow-up images, their fusion leading to the analysis of the lesion progression is complicated. The main aim regarding this topic is to develop a complex framework for detection of pathologic lesions on spine, with the main focus on osteoblastic and osteolystic lesions. Such system has to provide not only reliable segmentation of individual vertebrae and detection of their main regions but also the masking of their cortical bone, detection of their pathologic changes and their evaluation. Although this dissertation thesis is primarily oriented at the experimental part of medical image processing considering both the above mentioned topics, it deals with all necessary processing steps, i.e. preprocessing, image registration, post-processing and evaluation of results, leading to the future use of both frameworks in clinical practice. Since both frameworks were developed in a team, there are some chapters referring to the dissertation thesis of Milos Malinsky.
What scans we will read: imaging instrumentation trends in clinical oncology
Oncological diseases account for a significant portion of the burden on public healthcare systems with associated
costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific
morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-
invasively, so as to provide referring oncologists with essential information to support therapy management
decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards
integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/
CT), advanced MRI, optical or ultrasound imaging.
This perspective paper highlights a number of key technological and methodological advances in imaging
instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as
the hardware-based combination of complementary anatomical and molecular imaging. These include novel
detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system
developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing
methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging
in oncology patient management we introduce imaging methods with well-defined clinical applications and
potential for clinical translation. For each modality, we report first on the status quo and point to perceived
technological and methodological advances in a subsequent status go section. Considering the breadth and
dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the
majority of them being imaging experts with a background in physics and engineering, believe imaging methods
will be in a few years from now.
Overall, methodological and technological medical imaging advances are geared towards increased image contrast,
the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall
examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is
complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To
this end, we should accept diagnostic images as âdataâ, and â through the wider adoption of advanced analysis,
including machine learning approaches and a âbig dataâ concept â move to the next stage of non-invasive tumor
phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-
dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and
analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts
with a domain knowledge that will need to go beyond that of plain imaging
Artificial intelligence in cancer imaging: Clinical challenges and applications
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care
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