Iron is the most abundant trace metal in the brain and is essential for many biological processes, such as neurotransmitter synthesis and myelin formation. This thesis investigates small, multifocal hypointensities that are apparent on T2*- weighted (T2*w) MRI in the basal ganglia, where presumably most iron enters the brain via the blood-brain-barrier along the penetrating arteries. These basal ganglia T2*w hypointensities are believed to arise from iron-rich microvascular mineral deposits, which are frequently found in community-dwelling elderly subjects and are associated with age-related cognitive decline. This thesis documents the characteristic spatial distribution and morphology of basal ganglia T2*w hypointensities of 98 community-dwelling, elderly subjects in their seventies, as well as their imaging signatures on T1-weighted (T1w) and T2- weighted (T2w) MRI. A fully automated, novel method is introduced for the segmentation of basal ganglia T2*w hypointensities, which was developed to reduce the high intra- and inter-rater variability associated with current semi-automated segmentation methods and to facilitate the segmentation of these features in other single- and multi-centre studies. This thesis also presents a multi parametric quantitative MRI relaxometry methodology for conventional clinical MRI scanners that was developed and validated to improve the characterisation of brain iron. Lastly, this thesis describes the application of the developed methods in the segmentation of basal ganglia T2*w hypointensities of 243 community-dwelling participants of the Austrian Stroke Prevention Study Family (ASPS-Fam) and their analysis on R2* (=1/T2*) relaxation rate and Larmor frequency shift maps. This work confirms that basal ganglia T2*w hypointensities, especially in the globus pallidus, are potentially MRI markers of microvascular mineralization. Furthermore, the ASPS-Fam results show that basal ganglia mineral deposits mainly consist of paramagnetic particles, which presumably arise from an imbalance in the brain iron homeostasis. Hence, basal ganglia T2*w hypointensities are possibly an indicator of age-related microvascular dysfunction with iron accumulation, which might help to explain the variability of cognitive decline in normal ageing.

Optical coherence tomography (OCT) is becoming an increasingly important modality for the diagnosis and management of a variety of eye diseases, such as age-related macular degeneration (AMD), glaucoma, and diabetic macular edema (DME). Spectral domain OCT (SD-OCT), an advanced type of OCT, produces three dimensional high-resolution cross-sectional images and demonstrates delicate structure of the functional portion of posterior eye, including retina, choroid, and optic nerve head. As the clinical importance of OCT for retinal disease management and the need for quantitative and objective disease biomarkers grows, fully automated three-dimensional analysis of the retina has become desirable. Previously, our group has developed the Iowa Reference Algorithms (http://www.biomed-imaging.uiowa.edu/downloads/), a set of fully automated 3D segmentation algorithms for the analysis of retinal layer structures in subjects without retinal disease. This is the first method of segmenting and quantifying individual layers of the retina in three dimensions. However, in retinal disease, the normal architecture of the retina - specifically the outer retina - is disrupted. Fluid and deposits can accumulate, and normal tissue can be replaced by scar tissue. These abnormalities increase the irregularity of the retinal structure and make quantitative analysis in the image data extra challenging. In this work, we focus on the segmentation of the retina of patients with age-related macular degeneration, the most important cause of blindness and visual loss in the developed world. Though early and intermediate AMD results in some vision loss, the most devastating vision loss occurs in the two endstages of the disease, called geographic atrophy (GA) respectively choroidal neovascularization (CNV). In GA, because of pathological changes that are not fully understood, the retinal pigment epithelium disappears and photoreceptors lose this supporting tissue and degenerate. Second, in CNV, the growth of abnormal blood vessels originating from the choroidal vasculature causes fluid to enter the surrounding retina, causing disruption of the tissues and eventual visual loss. The severity and progress of early AMD is characterized by the formation of drusen and subretinal drusenoid deposits, structures containing photoreceptor metabolites - primarily lipofuscin - the more drusen the more severe the disease and the higher the risk of progressing to GAD or CNV. Thus, to improve the image guided management of AMD, we will study automated methods for segmenting and quantifying these intraretinal, subretinal and choroidal structures, including different types of abnormalities and layers, focusing on the outer retina. The major contributions of this thesis include: 1) developing an automated method of segmenting the choroid and quantifying the choroidal thickness in 3D OCT images; 2) developing an automated method of quantifying the presence of drusen in early and intermediate AMD; 3) developing an method of identifying the different ocular structures simultaneously; 4) studying the relationship among intraretinal, subretinal and choroidal structures.

<p> A new parameter-free texture feature-based seeded region growing algorithm is proposed in this dissertation for automated segmentation of organs in abdominal MR images. This algorithm requires that a user only mouse clicks twice to identify the upper left and lower right corners of a rectangular region of interest (ROI). With this given ROI, a seed point is automatically selected based on homogeneity criteria. Intensity as well as four texture features: 20 cooccurrence texture features, Gabor texture feature, and both 20 and 3D semivariogram texture features are extracted from the image and a seeded region growing algorithm is performed on these feature spaces. A threshold is then obtained by taking a lower value just before the one which results in an ' explosion '. An optional Snake post-processing tool is also provided to obtain better organ delineation. The comparative results of the texture features and intensity are reported using both normal digital images and abdominal MR images acquired from ten patients. Comparisons of Before and After Snake are also presented. Generally, Gabor texture feature is found to perform the best among all features . The experimental results of the proposed approach show that it is fast and accurate when combined with Gabor texture feature or intensity feature and should prove a boon to production radiological batch processing. </p> / Thesis / Doctor of Philosophy (PhD)

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Articular chondral lesions in the knee joint can be diagnosed at an early stage using MRI. Segmenting and visualizing lesions and the overall joint structure allows improved communication between the radiologist and referring physician. It can also be of help when determining diagnosis or conducting surgical planning. Although there are a variety of studies proving good results of segmentation of larger structures such as bone and cartilage in the knee, there are no studies available researching segmentation of articular cartilage lesions. Automating the segmentation will save time and money since manual segmentation is very time-consuming. In this thesis, a U-Net based convolutional neural network is used to perform automatic segmentation of chondral lesions located on the distal part of the femur, in the knee joint. Using two different techniques, batch normalization and dropout, a network was trained and tested using MRI sequences collected from Episurf Medical's database. The network was then evaluated using a segmentation approach and a detection approach. For the segmentation approach, the highest achieved dice coefficient and sensitivity of 0.4059 ± 0.1833 and 0.4591 ± 0.2387, was obtained using batch normalization and 260 training subjects, consisting of MRI sequence and corresponding masks. Using a detection approach, the predicted output could correctly identify 81.8% of the chondral lesions in the MRI sequences. Although there is a need for improvement of technique and datasets used in this thesis, the achieved results show prerequisites for future improvement and possible implementation. / Skador i knäledens brosk kan diagnostiseras i ett tidigt stadie med hjälp av MR. Segmentering och visualisering av skadorna, samt ledens struktur i helhet, bidrar till en förbättrad kommunikation mellan radiolog och remitterande läkare. Det kan också underlätta för att ställa diagnos eller utföra operationsplanering. I dagsläget finns flertalet studier som påvisar goda resultat för segmentering av större strukturer, t.ex. ben och brosk. Det finns dock få studier som studerar segmentering av skador i ledbrosk. Genom att automatisera segmenteringsprocessen kan både tid och pengar sparas. Detta eftersom att manuell segmentering är mycket tidskrävande. I detta arbete kommer ett U-Net baserat convolutional neural network att användas för att utföra automatisk segmentering av skador på distala femur i knäleden. Nätverket kommer att tränas med två olika tekniker, batch normalization och dropout. Nätverket kommer att tränas med data som är hämtad från Episurf Medicals databas och består av MR sekvenser. Nätverket kommer att tränas och utvärderas med hjälp av två metoder, en segmenteringsmetod och detekteringsmetod. Den högsta uppnådda dice koefficienten och sensitiviteten vid utvärderingen av segmenteringsmetoden uppmätte 0,4059 ± 0,1833 och 0,4591 ± 0,2387. Den upnåddes med hjälp av batch normalization och 260 MR sekvenser för träning och testning. För detektionsmetoden kunde programmet identifiera 81,8% av skadorna synliga på MR sekvenserna. Även om tekniken och datan som används behöver optimeras, så visar det uppnådda resultatet på bra förutsättningar för fortsatta studier och i framtiden möjligen även implementering av tekniken.