Noninvasive Imaging of Transgene Expression in Neurons Using Chemical Exchange Saturation Transfer MRI.

Advances in gene therapy, especially for brain diseases, have created new imaging demands for noninvasive monitoring of gene expression. While reporter gene imaging using co-expression of fluorescent protein-encoding gene has been widely developed, these conventional methods face significant limitations in longitudinal in vivo applications. Magnetic resonance imaging (MRI), specifically chemical exchange saturation transfer (CEST) MRI, provides a robust noninvasive alternative that offers unlimited depth penetration, reliable spatial resolution, and specificity toward particular molecules.

giRAff: an automated atlas segmentation tool adapted to single histological slices.

Conventional histology of the brain remains the gold standard in the analysis of animal models. In most biological studies, standard protocols usually involve producing a limited number of histological slices to be analyzed. These slices are often selected into a specific anatomical region of interest or around a specific pathological lesion.

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations.

Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain.

Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section.

Cell individualization has a vital role in digital pathology image analysis. Deep Learning is considered as an efficient tool for instance segmentation tasks, including cell individualization. However, the precision of the Deep Learning model relies on massive unbiased dataset and manual pixel-level annotations, which is labor intensive.