Development of machine learning schemes for segmentation, characterisation, and evolution prediction of white matter hyperintensities in structural brain MRI

White matter hyperintensities (WMH) are neuroradiological features seen in T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) brain magnetic resonance imaging (MRI) and have been commonly associated with stroke, ageing, dementia, and Alzheimer’s disease (AD) progression. As a marker of neuro-degenerative disease, WMH may change over time and follow the clinical condition of the patient. In contrast to the early longitudinal studies of WMH, recent studies have suggested that the progression of WMH may be a dynamic, non-linear process where different clusters of WMH may shrink, stay unchanged, or grow. In this thesis, these changes are referred to as the “evolution of WMH”. The main objective of this thesis is to develop machine learning methods for prediction of WMH evolution in structural brain MRI from one-time (baseline) assessment. Predicting the evolution of WMH is challenging because the rate and direction of WMH evolution varies greatly across previous studies. Furthermore, the evolution of WMH is a non-deterministic problem because some clinical factors that possibly influence it are still not known. In this thesis, different learning schemes of deep learning algorithm and data modalities are proposed to produce the best estimation of WMH evolution. Furthermore, a scheme to simulate the non-deterministic nature of WMH evolution, named auxiliary input, was also proposed. In addition to the development of prediction model for WMH evolution, machine learning methods for segmentation of early WMH, characterisation of WMH, and simulation of WMH progression and regression are also developed as parts of this thesis

A New Image Quantitative Method for Diagnosis and Therapeutic Response

abstract: Accurate quantitative information of tumor/lesion volume plays a critical role in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside. In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies. This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Multimodal techniques for biomedical image processing

Il lavoro di dottorato ha coinvolto tre principali aree di ricerca biomedica. Nella prima area, abbiamo mirato a valutare se le misure del tempo di rilassamento T1 in Risonanza Magnetica possono contribuire ad individuare dei predittori strutturali di lievi disturbi cognitivi in pazienti con forma Recidivante-Remittente di Sclerosi Multipla(RRMS). Ventinove controlli sani (HC) e quarantanove RRMS pazienti sono stati sottoposti a Risonanza magnetica a 3T per acquisire in maniera ottimale per la zona corticale e per la sostanza bianca (WML), i tempi di rilassamento T1 (rt), la conta delle lesioni e il volume. Nella WML e in quelle di tipo CL I (sostanza bianca - grigia mista), i T1 rt z-score sono risultati, significativamente, pi\uf9 lunghi rispetto ai tessuti dei controlli HC (p<0.001 e p<0.01, rispettivamente), indice di un\u2019impoverimento del tessuto cerebrale. L'analisi di regressione multivariata ha rivelato che: i T1 rt z-score nelle lesioni corticali sono predittori indipendenti del recupero della memoria a lungo termine (p=0.01), i T1 z -score nella lesioni corticali della materia bianca sono predittori indipendenti del deficit relativi all\u2019attenzione prolungata e all\u2019elaborazione delle informazioni (p=0,02) ; Nella seconda, descriviamo un suscettometro biomagnetico a temperatura ambiente in grado di quantificare il sovraccarico di ferro nel fegato. Tramite un campo magnetico modulato elettronicamente, il sistema riesce a misurare segnali magnetici 108 volte pi\uf9 piccoli del campo applicato. Il rumore meccanico del suscettometro a temperatura ambiente viene minimizzato e il drift termico viene monitorato da un sistema automatico di bilanciamento. Abbiamo testato e calibrato lo strumento utilizzando un fantoccio riempito con una soluzione di esacloruro esaidrato II di ferro, ottenendo come correlazione R = 0,98 tra la massima risposta del suscettometro e la concentrazione di ferro. Queste misure indicano che per garantire una buon funzionamento dello strumento con una variabilit\ue0 del segnale di uscita pari al 4-5%, eguale a circa 500ugr/gr di ferro, il tempo di acquisizione deve essere minore o uguale a 8 secondi. Nela terza area, un'analisi agli elementi finiti del modello 3D anatomicamente dettagliato del piede umano \ue8 il risultato finale della segmentazione 3D, secondo tecniche di ricostruzione applicate ad immagini standard DICOM di scansione a Tomografia Computerizzata, in congiunzione con la modellazione 3D assistita e dell\u2019analisi agli elementi finiti (FEA). In questo modello la reale morfologia del cuscinetto adiposo plantare \ue8 stato considerata: \ue8 stato dimostrato giocare un ruolo molto importante durante il contatto con il terreno. Per ottenere i dati sperimentali da confrontare con le predizioni del modello 3D del piede, un esame posturografico statico su una pedana baropodometrica \ue8 stato effettuato. La pressione sperimentale del contatto plantare \ue8 risultata, qualitativamente, comparabile con i risultati predetti dall\u2019analisi agli elementi finiti, principalmente, confrontando i valori sperimentali con i valori massimi delle pressioni in corrispondenza delle zona centrali del tallone e sotto le teste metatarsali.The PhD work involved three main biomedical research areas. In the first, we aimed at assessing whether T1 relaxometry measurements may help identifying structural predictors of mild cognitive impairments in patients with relapsing-remitting multiple sclerosis. Twenty-nine healthy controls and forty-nine RRMS patients underwent at high resolution 3T magnetic resonance imaging to obtain optimal cortical and white matter lesion count/volume as well as T1 relaxation times (rt). In WML and CL type I (mixed white-gray matter), T1 rt z-scores were significantly longer than in HC tissue (p<0.001 and p<0.01 respectively), indicating loss of structure. Multivariate analysis revealed T1 rt z-scores in CL type I were independent predictors of long term retrieval (p=0.01), T1 z-score relaxation time in white matter cortical lesions were independent predictors of sustained attention and information processing (p=0.02); In the second, we describe a biomagnetic susceptometer at room-temperature to quantify liver iron overload. By electronically modulated magnetic field, the magnetic system measure magnetic signal 108 times weaker than field applied. The mechanical noise of room-temperature susceptometer is cancelled and thermal drift is monitored by an automatic balance control system. We have tested and calibrated the system using cylindrical phantom filled with hexahydrated iron II choloride solution, obtaining the correlation (R=0.98) of the maximum variation in the responses of the susceptometer. These measures indicate that the acquisition time must be less than 8 seconds to guarantee an output signal variability to about 4-5%, equal to 500ugr/grwet of iron. In the third, a 3D anatomically detailed finite element analysis human foot model is final results of density segmentation 3D reconstruction techiniques applied in Computed Tomography(CT) scan DICOM standard images in conjunctions with 3D finite element analysis(FEA) modeling. In this model the real morphology of plantar fat pad has been considered: it was shown to play a very important role during the contact with the ground. To obtain the experimental data to compare the predictions of 3D foot model, a posturography static examination test on a baropodometric platform has been carried. The experimental plantar contact pressure is, qualitatively, comparable with FEA predicted results, nominally, the peak pressure value zones at the centre heel region and beneath the metatarsal heads

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Explainable deep learning classifiers for disease detection based on structural brain MRI data

In dieser Doktorarbeit wird die Frage untersucht, wie erfolgreich deep learning bei der Diagnostik von neurodegenerativen Erkrankungen unterstützen kann. In 5 experimentellen Studien wird die Anwendung von Convolutional Neural Networks (CNNs) auf Daten der Magnetresonanztomographie (MRT) untersucht. Ein Schwerpunkt wird dabei auf die Erklärbarkeit der eigentlich intransparenten Modelle gelegt. Mit Hilfe von Methoden der erklärbaren künstlichen Intelligenz (KI) werden Heatmaps erstellt, die die Relevanz einzelner Bildbereiche für das Modell darstellen. Die 5 Studien dieser Dissertation zeigen das Potenzial von CNNs zur Krankheitserkennung auf neurologischen MRT, insbesondere bei der Kombination mit Methoden der erklärbaren KI. Mehrere Herausforderungen wurden in den Studien aufgezeigt und Lösungsansätze in den Experimenten evaluiert. Über alle Studien hinweg haben CNNs gute Klassifikationsgenauigkeiten erzielt und konnten durch den Vergleich von Heatmaps zur klinischen Literatur validiert werden. Weiterhin wurde eine neue CNN Architektur entwickelt, spezialisiert auf die räumlichen Eigenschaften von Gehirn MRT Bildern.Deep learning and especially convolutional neural networks (CNNs) have a high potential of being implemented into clinical decision support software for tasks such as diagnosis and prediction of disease courses. This thesis has studied the application of CNNs on structural MRI data for diagnosing neurological diseases. Specifically, multiple sclerosis and Alzheimer’s disease were used as classification targets due to their high prevalence, data availability and apparent biomarkers in structural MRI data. The classification task is challenging since pathology can be highly individual and difficult for human experts to detect and due to small sample sizes, which are caused by the high acquisition cost and sensitivity of medical imaging data. A roadblock in adopting CNNs to clinical practice is their lack of interpretability. Therefore, after optimizing the machine learning models for predictive performance (e.g. balanced accuracy), we have employed explainability methods to study the reliability and validity of the trained models. The deep learning models achieved good predictive performance of over 87% balanced accuracy on all tasks and the explainability heatmaps showed coherence with known clinical biomarkers for both disorders. Explainability methods were compared quantitatively using brain atlases and shortcomings regarding their robustness were revealed. Further investigations showed clear benefits of transfer-learning and image registration on the model performance. Lastly, a new CNN layer type was introduced, which incorporates a prior on the spatial homogeneity of neuro-MRI data. CNNs excel when used on natural images which possess spatial heterogeneity, and even though MRI data and natural images share computational similarities, the composition and orientation of neuro-MRI is very distinct. The introduced patch-individual filter (PIF) layer breaks the assumption of spatial invariance of CNNs and reduces convergence time on different data sets without reducing predictive performance. The presented work highlights many challenges that CNNs for disease diagnosis face on MRI data and defines as well as tests strategies to overcome those

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.