Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studying organ development, drug discovery, and toxicity assessment. Recent microscopy techniques provide a potent tool to acquire organoid morphology features, but manual image analysis remains a labor and time-intensive process. Thus, this paper proposes a comprehensive pipeline for microscopy analysis that leverages the SegmentAnything to precisely demarcate individual organoids. Additionally, we introduce a set of morphological properties, including perimeter, area, radius, non-smoothness, and non-circularity, allowing researchers to analyze the organoid structures quantitatively and automatically. To validate the effectiveness of our approach, we conducted tests on bright-field images of human induced Pluripotent Stem Cells (iPSCs) derived Neural-Epithelial (NE) organoids. The results obtained from our automatic pipeline closely align with manual organoid detection and measurement, showcasing the capability of our proposed method in accelerating organoids morphology analysis.
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardiac MR technique providing magnitude and phase information of the myocardial motion simultaneously. However, long acquisition time limits the usage of this technique by causing breathing artifacts, while shortening the time causes low temporal resolution and may provide an inaccurate assessment of cardiac motion. In this study, we proposed a frame synthesis algorithm to increase the temporal resolution of 3Dir MVM data. Our algorithm is featured by 1) three attention-based encoders which accept magnitude images, phase images, and myocardium segmentation masks respectively as inputs; 2) three decoders that output the interpolated frames and corresponding myocardium segmentation results; and 3) loss functions highlighting myocardium pixels. Our algorithm can not only increase the temporal resolution 3Dir MVMs, but can also generates the myocardium segmentation results at the same time.
Objective and efficient diagnosis of Alzheimer’s disease (AD) has been a major topic with extensive researches in recent years, and some promising results have been shown for imaging markers using magnetic resonance imaging (MRI) data. Beside conventional machine learning methods, deep learning based methods have been developed in several studies, where layer-by-layer neural network settings were purposed to extract features for disease classification from the patches or whole images. However, as the disease develops from subcortical nuclei to cortical regions, specific brain regions with morphological changes might contribute to the diagnosis of disease progress. Therefore, we propose a novel spatial and depth weighted neural network structure to extract effective features, and further improve the performance of AD diagnosis. Specifically, we first use group comparison to detect the most distinctive AD-related landmarks, and then sample landmark-based image patches as our training data. In the model structure, with a 15-layer DenseNet as backbone, we introduce a attention bypass to estimate the spatial weights in the image space to guide the network to focus on specific regions. A squeeze-and-excitation (SE) mechanism is also adopted to further weight the feature map channels. We used 2335 subjects from public datasets (i.e., ADNI-1, ADNI-2 and ADNI-GO) for experiment and results show that our framework achieves 90.02% accuracy, 81.25% sensitivity, and 96.33% specificity in diagnosis AD patients from normal controls.
Obsessive-compulsive disorder (OCD) is a mental disorder characterized by repeated thoughts or behaviors, which is also associated with anxiety and tics. Clinically, the diagnosis of OCD mainly depends on subjects symptoms and psychological rating scales. In this study, we proposed an imaging based diagnosis method using functional MRI to classify OCD patients and healthy controls, with a novel log Euclidean based kernel Principal Component Analysis (PCA) as feature extractor. In particular, functional connectivity (FC) matrix was computed for each subject as the FC correlations of each pair of brain regions of interest. To better reduce feature dimension and extract the most discriminative features, we propose to use log Euclidean geodesic distance as the distance of two matrices and apply a Gaussian kernel PCA to FC matrix for feature extraction, given the graph Laplacian matrix of a FC matrix is symmetric positive define (SPD) matrix and the set of SPD matrix forms a Riemannian manifold. We further employed gradient boosted decision trees (XGBoost) to classify the features extracted from log Euclidean based kernel PCA to diagnosis patient groups. Results show that the classification accuracy reaches 91.8% with 90.7% sensitivity and 92.6% specificity, which outperforms current start-of-the-art imaging based diagnosis methods such as 85% in an EEG study. Next, by evaluating the feature importance in the classifier, we found that most contributed connections are cerebellum related, such as cerebellar vermis. These findings may help the understanding of pathology of OCD and provide a surrogate means for clinical diagnosis.
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