Spelling suggestions: "subject:"automated segmentation"" "subject:"utomated segmentation""
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Characterisation and segmentation of basal ganglia mineralization in normal ageing with multimodal structural MRIGlatz, Andreas January 2016 (has links)
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.
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Automated segmentation and analysis of layers and structures of human posterior eyeZhang, Li 01 December 2015 (has links)
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.
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An Automated Human Organ Segmentation Technique for Abdominal Magnetic Resonance ImagesWu, Jie 03 1900 (has links)
<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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Techniques for Finite Element Modeling and Remodeling of Bones with Applications to Pig SkullsZhu, Zimo January 2017 (has links)
No description available.
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Robotic Pruning for Indoor Indeterminate PlantsSrivastava, Chhayank 01 July 2024 (has links)
This thesis presents an innovative agricultural automation technique which focuses on addressing the significant perception challenges posed by occlusion within environments such as farms and greenhouses. Automated systems tasked with duties like pruning face considerable difficulties due to occlusion, complicating the accurate identification of plant features. To tackle these challenges, this work introduces a novel approach utilizing a LiDAR camera mounted on a robot arm, enhancing the system's ability to scan plants and dynamically adjust the arm's trajectory based on machine learning-derived segmentation. This adjustment significantly increases the detection area of plant features, improving identification accuracy and efficiency.
Building on foreground isolation and instance segmentation, the thesis then presents an automated method for identifying optimal pruning points using best pose view images of indeterminate tomato plants. By integrating advanced image processing techniques, the proposed method ensures the pruning process by targeting branches with the highest leaf load.
Experimental validation of the proposed method was conducted in a simulated environment, where it demonstrated substantially enhanced performance. In terms of pruning point identification, the method achieved impressive results with 94% precision, 90% recall, and 92% F1 score for foreground isolation. Furthermore, the segmentation of isolated images significantly outperformed non-isolated ones, with improvements exceeding 30%, 27%, and 30% in precision, recall, and F1 metrics, respectively. This validation also confirmed the method's effectiveness in accurately identifying pruning points, achieving a 67% accuracy rate when compared against manually identified pruning points. These results underscore the robustness and reliability of the approach in automating pruning processes in agricultural settings. / Master of Science / This thesis explores new methods for improving automated farming systems, particularly focusing on enhancing tasks like pruning where visibility of plant features can be significantly obstructed by overlapping leaves and branches. Central to this study is the development of an innovative approach using a special camera mounted on a robotic arm, which scans plants to determine the best vantage points for precise interactions. This setup not only identifies the optimal positions for viewing but also adjusts the robot's movements in real-time to ensure it can accurately perform pruning task.
The innovative approach employed here leverages advanced technology to dynamically adjust the trajectory of the robotic arm based on real-time imaging. This enables the robot to better detect essential features of plants, which is crucial to make informed decision of where to prune the plant. By improving the robot's ability to clearly see and interact with plants, the system facilitates more precise and efficient operations.
Tests conducted in simulated environments have demonstrated that this method significantly enhances the robot's capability to isolate and identify plant features accurately. These improvements make it possible for the robot to subsequently identify pruning points, potentially reducing the time and labor typically required in traditional manual operations. Overall, this research indicates that integrating advanced sensing and machine learning technologies into agricultural automation can revolutionize farming practices, making them more efficient and less dependent on human labor, especially in environments where traditional methods are less effective.
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Deep Learning-Based Automated Segmentation and Detection of Chondral Lesions on the Distal FemurLindemalm Karlsson, Josefin January 2019 (has links)
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.
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