I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients have diverse shapes that reveal important biomechanical and bio-rheological characteristics, e.g. their density, fragility, adhesive properties, etc. Hence, having an objective and effective way of RBC shape quantification and classification will lead to better insights and eventual better prognosis of the disease. To this end, we have developed an automated, high-throughput, ex-vivo RBC shape classification framework that consists of three stages. First, we present an automatic hierarchical RBC extraction method to detect the RBC region (ROI) from the background, and then separate touching RBCs in the ROI images by applying an improved random walk method based on automatic seed generation. Second, we apply a mask-based RBC patch-size normalization method to normalize the variant size of segmented single RBC patches into uniform size. Third, we employ deep convolutional neural networks (CNNs) to realize RBC classification; the alternating convolution and pooling operations can deal with non-linear and complex patterns. Furthermore, we investigate the specific shape factor quantification for the classified RBC image data in order to develop a general multiscale shape analysis. We perform several experiments on raw microscopy image datasets from 8 SCD patients (over 7,000 single RBC images) through a 5-fold cross validation method both for oxygenated and deoxygenated RBCs. We demonstrate that the proposed framework can successfully classify sickle shape RBCs in an automated manner with high accuracy, and we also provide the corresponding shape factor analysis, which can be used synergistically with the CNN analysis for more robust predictions. Moreover, the trained deep CNN exhibits good performance even for a deoxygenated dataset and distinguishes the subtle differences in texture alteration inside the oxygenated and deoxygenated RBCs.

/pdf/a-deep-convolutional-neural-network-for-classification-of-21zh5lt2vb.pdf

A deep convolutional neural network for classification of red blood cells in sickle cell anemia

Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learning-based models. We report a Nuclear Segmentation Tool (NuSeT) based on deep learning that accurately segments nuclei across multiple types of fluorescence imaging data. Using a hybrid network consisting of U-Net and Region Proposal Networks (RPN), followed by a watershed step, we have achieved superior performance in detecting and delineating nuclear boundaries in 2D and 3D images of varying complexities. By using foreground normalization and additional training on synthetic images containing non-cellular artifacts, NuSeT improves nuclear detection and reduces false positives. NuSeT addresses common challenges in nuclear segmentation such as variability in nuclear signal and shape, limited training sample size, and sample preparation artifacts. Compared to other segmentation models, NuSeT consistently fares better in generating accurate segmentation masks and assigning boundaries for touching nuclei.

/pdf/nuset-a-deep-learning-tool-for-reliably-separating-and-4hixw9he30.pdf

NuSeT: A deep learning tool for reliably separating and analyzing crowded cells.

Analysis of live cell images: Methods, tools and opportunities.

Abstract The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Owing to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysis of dynamics occurring in crowded and complex environments still lags behind the acquisition of microscopic image sequences. Here we present a framework based on geometric deep learning that achieves the accurate estimation of dynamical properties in various biologically relevant scenarios. This deep-learning approach relies on a graph neural network enhanced by attention-based components. By processing object features with geometric priors, the network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties. We demonstrate the flexibility and reliability of this approach by applying it to real and simulated data corresponding to a broad range of biological experiments. 

/pdf/geometric-deep-learning-reveals-the-spatiotemporal-features-3j5p7eat.pdf

Geometric deep learning reveals the spatiotemporal features of microscopic motion

Individual cells play essential roles in the biological processes of the brain. The number of neurons changes during both normal development and disease progression. High-resolution imaging has made it possible to directly count cells. However, the automatic and precise segmentation of touching cells continues to be a major challenge for massive and highly complex datasets. Thus, an integrative cut (iCut) algorithm, which combines information regarding spatial location and intervening and concave contours with the established normalized cut, has been developed. iCut involves two key steps: (1) a weighting matrix is first constructed with the abovementioned information regarding the touching cells and (2) a normalized cut algorithm that uses the weighting matrix is implemented to separate the touching cells into isolated cells. This novel algorithm was evaluated using two types of data: the open SIMCEP benchmark dataset and our micro-optical imaging dataset from a Nissl-stained mouse brain. It has achieved a promising recall/precision of 91.2 ± 2.1%/94.1 ± 1.8% and 86.8 ± 4.1%/87.5 ± 5.7%, respectively, for the two datasets. As quantified using the harmonic mean of recall and precision, the accuracy of iCut is higher than that of some state-of-the-art algorithms. The better performance of this fully automated algorithm can benefit studies of brain cytoarchitecture.

/pdf/icut-an-integrative-cut-algorithm-enables-accurate-1r5oh175h0.pdf

iCut: an Integrative Cut Algorithm Enables Accurate Segmentation of Touching Cells

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iCut: an Integrative Cut Algorithm Enables Accurate Segmentation of Touching Cells