MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks

In gynecologic cancer imaging, multiple magnetic resonance imaging (MRI) sequences are acquired per patient to reveal different tissue characteristics. However, after image acquisition, the anatomical structures can be misaligned in the various sequences due to changing patient location in the scanner and organ movements. The co-registration process aims to align the sequences to allow for multi-sequential tumor imaging analysis. However, automatic co-registration often leads to unsatisfying results. To address this problem, we propose the web-based application MuSIC (Multi-Sequential Interactive Co-registration). The approach allows medical experts to co-register multiple sequences simultaneously based on a pre-defined segmentation mask generated for one of the sequences. Our contributions lie in our proposed workflow. First, a shape matching algorithm based on dual annealing searches for the tumor position in each sequence. The user can then interactively adapt the proposed segmentation positions if needed. During this procedure, we include a multi-modal magic lens visualization for visual quality assessment. Then, we register the volumes based on the segmentation mask positions. We allow for both rigid and deformable registration. Finally, we conducted a usability analysis with seven medical and machine learning experts to verify the utility of our approach. Our participants highly appreciate the multi-sequential setup and see themselves using MuSIC in the future. Best Paper Honorable Mention at VCBM2022publishedVersio

Registration of serial sections: An evaluation method based on distortions of the ground truths

Registration of histological serial sections is a challenging task. Serial sections exhibit distortions and damage from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations extremely difficult. This work proposes methods for ground-truth-based evaluation of registrations. Firstly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Secondly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. Both under- and over-registration become evident in our evaluations. We also survey existing validation efforts. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available.Comment: Supplemental data available under https://zenodo.org/record/428244

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

“It was not Death” : The Poetic Career of the Chronotope

As Bakhtin noted, chronotopes arise from the density and fusion of temporal and spatial indicators. In prose narrative, the density of temporal and spatial indicators arises as a natural consequence of setting scenes and explaining action, and those indicators are fused by the centripetal forces of plot, character and so on that encourage us to read the various elements of the text as aspects of a coherent story and world. In non-narrative poetry, however, there is no story to drive the setting of scene or generation of character; there may not even be scene or character. As a result, temporal and spatial indicators can be quite sparse, and there may be little centripetal force to encourage their fusion. In a textual environment bereft of character, plot, scene, in which even the centripetal forces of syntax are frayed by linebreaks and other poetic devices, how can chronotopes form and function? [...] In the centripetal environment afforded by most prose narratives, the stable chronotopes and the relationships among them define consciousness, world and values. In the centrifugal environment of non-narrative poetry, chronotopes flicker and flow in a series of hints, glimpses, dissolves, defining consciousness, world and values via evanescence rather than stability. However, as I hope to show below, the evanescence of chronotopes in non-narrative poetry can be as central to the vitality and meaning of those texts as the stability of chronotopes is to the vitality and meaning of prose narratives

Fusion and Analysis of Multidimensional Medical Image Data

Analýza medicínských obrazů je předmětem základního výzkumu již řadu let. Za tu dobu bylo v této oblasti publikováno mnoho výzkumných prací zabývajících se dílčími částmi jako je rekonstrukce obrazů, restaurace, segmentace, klasifikace, registrace (lícování) a fúze. Kromě obecného úvodu, pojednává tato disertační práce o dvou medicínsky orientovaných tématech, jež byla formulována ve spolupráci s Philips Netherland BV, divizí Philips Healthcare. První téma je zaměřeno na oblast zpracování obrazů subtrakční angiografie dolních končetin člověka získaných pomocí výpočetní X-Ray tomografie (CT). Subtrakční angiografie je obvykle využívaná při podezření na periferní cévní onemocnění (PAOD) nebo při akutním poškození dolních končetin jako jsou fraktury apod. Současné komerční metody nejsou dostatečně spolehlivé už v předzpracování, jako je například odstranění pacientského stolu, pokrývky, dlahy, apod. Spolehlivost a přesnost identifikace cév v subtrahovaných datech vedoucích v blízkosti kostí je v důsledku Partial Volume artefaktu rovněž nízká. Automatické odstranění kalcifikací nebo detekce malých cév doplňujících nezbytnou informaci o náhradním zásobení dolních končetin krví v případě přerušení hlavních zásobujících cév v současné době rovněž nesplňují kritéria pro plně automatické zpracování. Proto hlavním cílem týkající se tohoto tématu bylo vyvinout automatický systém, který by mohl současné nedostatky v CTSA vyšetření odstranit. Druhé téma je orientováno na identifikaci patologických změn na páteři člověka v CT obrazech se zaměřením na osteolytické a osteoblastické léze u jednotlivých obratlů. Tyto změny obvykle nastávají v důsledků postižení metastazujícím procesem rakovinového onemocnění. Pro detekci patologických změn je pak potřeba identifikace a segmentace jednotlivých obratlů. Přesnost analýzy jednotlivých lézí však závisí rovněž na správné identifikaci těla a zadních segmentů u jednotlivých obratlů a na segmentaci trabekulárního centra obratlů, tj. odstranění kortikální kosti. Během léčby mohou být pacienti skenováni vícekrát, obvykle s několika-mesíčním odstupem. Hodnocení případného vývoje již detekovaných patologických změn pak logicky vychází ze správné detekce patologií v jednotlivých obratlech korespondujících si v jednotlivých akvizicích. Jelikož jsou příslušné obratle v jednotlivých akvizicích obvykle na různé pozici, jejich fúze, vedoucí k analýze časového vývoje detekovaných patologií, je komplikovaná. Požadovaným výsledkem v tomto tématu je vytvoření komplexního systému pro detekci patologických změn v páteři, především osteoblastických a osteolytických lézí. Takový systém tedy musí umožnovat jak segmentaci jednotlivých obratlů, jejich automatické rozdělení na hlavní části a odstranění kortikální kosti, tak také detekci patologických změn a jejich hodnocení. Ačkoliv je tato disertační práce v obou výše zmíněných tématech primárně zaměřena na experimentální část zpracování medicínských obrazů, zabývá se všemi nezbytnými kroky, jako je předzpracování, registrace, dodatečné zpracování a hodnocení výsledků, vedoucími k možné aplikovatelnosti obou systému v klinické praxi. Jelikož oba systémy byly řešeny v rámci týmové spolupráce jako celek, u obou témat jsou pro některé konkrétní kroky uvedeny odkazy na doktorskou práci Miloše Malínského.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 patients’ 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.

A comparative study of surrogate musculoskeletal models using various neural network configurations

Title from PDF of title page, viewed on August 13, 2013Thesis advisor: Reza R. DerakhshaniVitaIncludes bibliographic references (pages 85-88)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The central idea in musculoskeletal modeling is to be able to predict body-level (e.g. muscle forces) as well as tissue-level information (tissue-level stress, strain, etc.). To develop computationally efficient techniques to analyze such models, surrogate models have been introduced which concurrently predict both body-level and tissue-level information using multi-body and finite-element analysis, respectively. However, this kind of surrogate model is not an optimum solution as it involves the usage of finite element models which are computation intensive and involve complex meshing methods especially during real-time movement simulations. An alternative surrogate modeling method is the use of artificial neural networks in place of finite-element models. The ultimate objective of this research is to predict tissue-level stresses experienced by the cartilage and ligaments during movement and achieve concurrent simulation of muscle force and tissue stress using various surrogate neural network models, where stresses obtained from finite-element models provide the frame of reference. Over the last decade, neural networks have been successfully implemented in several biomechanical modeling applications. Their adaptive ability to learn from examples, simple implementation techniques, and fast simulation times make neural networks versatile and robust when compared to other techniques. The neural network models are trained with reaction forces from multi-body models and stresses from finite element models obtained at the interested elements. Several configurations of static and dynamic neural networks are modeled, and accuracies close to 93% were achieved, where the correlation coefficient is the chosen measure of goodness. Using neural networks, the simulation time was reduced nearly 40,000 times when compared to the finite-element models. This study also confirms theoretical concepts that special network configurations--including average committee, stacked generalization, and negative correlation learning--provide considerably better results when compared to individual networks themselves.Introduction -- Methods -- Results -- Conclusion -- Future work -- Appendix A. Various linear and non-linear modeling techniques -- Appendix B. Error analysi

Robust Motion and Distortion Correction of Diffusion-Weighted MR Images

Effective image-based correction of motion and other acquisition artifacts became an essential step in diffusion-weighted Magnetic Resonance Imaging (MRI) analysis as the micro-structural tissue analysis advances towards higher-order models. These come with increasing demands on the number of acquired images and the diffusion strength (b-value) yielding lower signal-to-noise ratios (SNR) and a higher susceptibility to artifacts. These conditions, however, render the current image-based correction schemes, which act retrospectively on the acquired images through pairwise registration, more and more ineffective. Following the hypothesis, that a more consequent exploitation of the different intensity relationships between the volumes would reduce registration outliers, a novel correction scheme based on memetic search is proposed. This scheme allows for incorporating all single image metrics into a multi-objective optimization approach. To allow a quantitative evaluation of registration precision, realistic synthetic data are constructed by extending a diffusion MRI simulation framework by motion and eddy-currents-caused artifacts. The increased robustness and efficacy of the multi-objective registration method is demonstrated on the synthetic as well as in-vivo datasets at different levels of motion and other acquisition artifacts. In contrast to the state-of-the-art methods, the average target registration error (TRE) remained below the single voxel size also at high b-values (3000 s.mm-2) and low signal-to-noise ratio in the moderately artifacted datasets. In the more severely artifacted data, the multi-objective method was able to eliminate most of the registration outliers of the state-of-the-art methods, yielding an average TRE below the double voxel size. In the in-vivo data, the increased precision manifested itself in the scalar measures as well as the fiber orientation derived from the higher-order Neurite Orientation Dispersion and Density Imaging (NODDI) model. For the neuronal fiber tracts reconstructed on the data after correction, the proposed method most closely resembled the ground-truth. The proposed multi-objective method has not only impact on the evaluation of higher-order diffusion models as well as fiber tractography and connectomics, but could also find application to challenging image registration problems in general