The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects’ cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.
The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multi-atlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non-Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.
Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.
Cerebellar dysfunction can lead to a wide range of movement disorders. Studying the cerebellar atrophy pattern associated with different cerebellar disease types can potentially help in diagnosis, prognosis, and treatment planning. In this paper, we present a landmark based shape analysis pipeline to classify healthy control and different ataxia types and to visualize the characteristic cerebellar atrophy patterns associated with different types. A highly informative feature representation of the cerebellar structure is constructed by extracting dense homologous landmarks on the boundary surfaces of cerebellar sub-structures. A diagnosis group classifier based on this representation is built using partial least square dimension reduction and regularized linear discriminant analysis. The characteristic atrophy pattern for an ataxia type is visualized by sampling along the discriminant direction between healthy controls and the ataxia type. Experimental results show that the proposed method can successfully classify healthy controls and different ataxia types. The visualized cerebellar atrophy patterns were consistent with the regional volume decreases observed in previous studies, but the proposed method provides intuitive and detailed understanding about changes of overall size and shape of the cerebellum, as well as that of individual lobules.
KEYWORDS: Associative arrays, Shape analysis, Binary data, Control systems, Cerebellum, Principal component analysis, Chemical species, Feature extraction, Space operations, Actinium
Many types of diseases manifest themselves as observable changes in the shape of the affected organs. Using shape classification, we can look for signs of disease and discover relationships between diseases. We formulate the problem of shape classification in a holistic framework that utilizes a lossless scalar field representation and a non-parametric classification based on sparse recovery. This framework generalizes over certain classes of unseen shapes while using the full information of the shape, bypassing feature extraction. The output of the method is the class whose combination of exemplars most closely approximates the shape, and furthermore, the algorithm returns the most similar exemplars along with their similarity to the shape, which makes the result simple to interpret. Our results show that the method offers accurate classification between three cerebellar diseases and controls in a database of cerebellar ataxia patients. For reproducible comparison, promising results are presented on publicly available 2D datasets, including the ETH-80 dataset where the method achieves 88.4% classification accuracy.
With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image
analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step
in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and
adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting
cells in uorescence images of conuent cell monolayers. This method addresses several challenges through a
combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as
initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2)
To deal with different defects in the uorescence images, the cell junctions are enhanced by applying an orderstatistic
filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes
robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The
automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient
over all distinguishable cells is 0:88.
Automatic labeling of the gyri and sulci on the cortical surface is important for studying cortical morphology
and brain functions within populations. A method to simultaneously label gyral regions and extract sulcal
curves is proposed. Assuming that the gyral regions parcellate the whole cortical surface into contiguous regions
with certain fixed topology, the proposed method labels the subject cortical surface by deformably registering
a network of curves that form the boundary of gyral regions to the subject cortical surface. In the registration
process, the curves are encouraged to follow the fine details of the sulcal geometry and to observe the shape
statistics learned from training data. Using the framework of probabilistic point set registration methods, the
proposed algorithm finds the sulcal curve network that maximizes the posterior probability by Expectation-Maximization (EM). The automatic labeling method was evaluated on 15 cortical surfaces using a leave-one-out
strategy. Quantitative error analysis is carried out on both labeled regions and major sulcal curves.
We present a method for automatically registering multi-view range images. For each range image in the data set,
corresponding point sets of its feature points are established, on the rest of the scans, by matching regional point
descriptors. Then we build a statistic model to estimate the likelihood of the image overlapping with each of the rest ones
to select candidate overlapping range images. Candidate overlapping pairs of range images are verified and registered by
enforcing geometric constraints on point matches and ICP algorithm. Finally the absolute positions of all range images
are established and fine-tuned. Experiment result demonstrates efficiency of the method in picking up potential
overlapping images, and establishing initial transformation between overlapping pairs. The proposed method offers a
good solution to automatically building complete 3D model of objects from unordered 3D scan data sets.
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