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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images
摘要： Oligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.
关键词： neural network,microscopic fluorescence imaging,myelin,deep learning,LeNet
更新于2025-09-23 15:22:29
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High-resolution imaging of distinct human corpus callosum microstructure and topography of structural connectivity to cortices at high field
摘要： Characterization of the microstructural properties and topography of the human corpus callosum (CC) is key to understanding interhemispheric neural communication and brain function. In this work, we tested the hypothesis that high-resolution T1 relaxometry at high field has adequate sensitivity and specificity for characterizing microstructural properties of the human CC, and elucidating the structural connectivity of the callosal fibers to the cortices of origin. The high-resolution parametric T1 images acquired from healthy subjects (N = 16) at 7 T clearly showed a consistent T1 distribution among individuals with substantial variation along the human CC axis, which is highly similar to the spatial patterns of myelin density and myelinated axon size based on the histology study. Compared to the anterior part of the CC, the posterior midbody and splenium had significantly higher T1 values. In conjunction with T1-based classification method, the splenial T1 values were decoded more reliably compared to a conventional partitioning method, showing a much higher T1 value in the inferior splenium than in the middle/superior splenium. Moreover, the T1 profile of the callosal subdivision represented the topology of the fiber connectivity to the projected cortical regions: the fibers in the posterior midbody and inferior splenium with a higher T1 (inferring a larger axon size) were mainly connected to motor–sensory and visual cortical areas, respectively; in contrast, the fibers in the anterior/posterior CC with a lower T1 (inferring a smaller axon size) were primarily connected to the frontal/parietal–temporal areas. These findings indicate that high-resolution T1 relaxometry imaging could provide a complementary and robust neuroimaging tool, useful for exploring the complex tissue properties and topographic organization of the human corpus callosum.
关键词： Myelin density,Structural connectivity,Topography,Parametric T1 MRI,Corpus callosum,Axon size
更新于2025-09-09 09:28:46