Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors

This study addresses the challenge of classifying cell shapes from noisy contours, such as those obtained through cell instance segmentation of histological images. We assess the performance of various features for shape classification, including Elliptical Fourier Descriptors, curvature features, and lower dimensional representations. Using an annotated synthetic dataset of noisy contours, we identify the most suitable shape descriptors and apply them to a set of real images for qualitative analysis. Our aim is to provide a comprehensive evaluation of descriptors for classifying cell shapes, which can support cell type identification and tissue characterization-critical tasks in both biological research and histopathological assessments.

CISCA and CytoDArk0: a Cell Instance Segmentation and Classification method for histo(patho)logical image Analyses and a new, open, Nissl-stained dataset for brain cytoarchitecture studies

Delineating and classifying individual cells in microscopy tissue images is a complex task, yet it is a pivotal endeavor in various medical and biological investigations. We propose a new deep learning framework (CISCA) for automatic cell instance segmentation and classification in histological slices to support detailed morphological and structural analysis or straightforward cell counting in digital pathology workflows and brain cytoarchitecture studies. At the core of CISCA lies a network architecture featuring a lightweight U-Net with three heads in the decoder. The first head classifies pixels into boundaries between neighboring cells, cell bodies, and background, while the second head regresses four distance maps along four directions. The network outputs from the first and second heads are integrated through a tailored post-processing step, which ultimately yields the segmentation of individual cells. A third head enables simultaneous classification of cells into relevant classes, if required. We showcase the effectiveness of our method using four datasets, including CoNIC, PanNuke, and MoNuSeg, which are publicly available H\&E datasets. Additionally, we introduce CytoDArk0, a novel dataset consisting of Nissl-stained images of the cortex, cerebellum, and hippocampus from mammals belonging to the orders Cetartiodactyla and Primates. We evaluate CISCA in comparison to other state-of-the-art methods, demonstrating CISCA's robustness and accuracy in segmenting and classifying cells across diverse tissue types, magnifications, and staining techniques.

Revealing Cortical Layers In Histological Brain Images With Self-Supervised Graph Convolutional Networks Applied To Cell-Graphs

Identifying cerebral cortex layers is crucial for comparative studies of the cytoarchitecture aiming at providing insights into the relations between brain structure and function across species. The absence of extensive annotated datasets typically limits the adoption of machine learning approaches, leading to the manual delineation of cortical layers by neuroanatomists. We introduce a self-supervised approach to detect layers in 2D Nissl-stained histological slices of the cerebral cortex. It starts with the segmentation of individual cells and the creation of an attributed cell-graph. A self-supervised graph convolutional network generates cell embeddings that encode morphological and structural traits of the cellular environment and are exploited by a community detection algorithm for the final layering. Our method, the first self-supervised of its kind with no spatial transcriptomics data involved, holds the potential to accelerate cytoarchitecture analyses, sidestepping annotation needs and advancing cross-species investigation.

NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images

Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells.

MR-NOM: Multi-scale Resolution of Neuronal cells in Nissl-stained histological slices via deliberate Over-segmentation and Merging

In comparative neuroanatomy, the characterization of brain cytoarchitecture is critical to a better understanding of brain structure and function, as it helps to distill information on the development, evolution, and distinctive features of different populations. The automatic segmentation of individual brain cells is a primary prerequisite and yet remains challenging. A new method (MR-NOM) was developed for the instance segmentation of cells in Nissl-stained histological images of the brain. MR-NOM exploits a multi-scale approach to deliberately over-segment the cells into superpixels and subsequently merge them via a classifier based on shape, structure, and intensity features. The method was tested on images of the cerebral cortex, proving successful in dealing with cells of varying characteristics that partially touch or overlap, showing better performance than two state-of-the-art methods.

Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances.