Accurate Neonatal Face Detection for Improved Pain Classification in the Challenging NICU Setting.

There is a tendency for object detection systems using off-the-shelf algorithms to fail when deployed in complex scenes. The present work describes a case for detecting facial expression in post-surgical neonates (newborns) as a modality for predicting and classifying severe pain in the Neonatal Intensive Care Unit (NICU). Our initial testing showed that both an off-the-shelf face detector and a machine learning algorithm trained on adult faces failed to detect facial expression of neonates in the NICU. We improved accuracy in this complex scene by training a state-of-the-art "You-Only-Look-Once" (YOLO) face detection model using the USF-MNPAD-I dataset of neonate faces. At run-time our trained YOLO model showed a difference of 8.6% mean Average Precision (mAP) and 21.2% Area under the ROC Curve (AUC) for automatic classification of neonatal pain compared with manual pain scoring by NICU nurses. Given the challenges, time and effort associated with collecting ground truth from the faces of post-surgical neonates, here we share the weights from training our YOLO model with these facial expression data. These weights can facilitate the further development of accurate strategies for detecting facial expression, which can be used to predict the time to pain onset in combination with other sensory modalities (body movements, crying frequency, vital signs). Reliable predictions of time to pain onset in turn create a therapeutic window of time wherein NICU nurses and providers can implement safe and effective strategies to mitigate severe pain in this vulnerable patient population.

A novel deep learning-based method for automatic stereology of microglia cells from low magnification images.

Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.

Novel stereological method for estimation of cell counts in 3D collagen scaffolds.

Current methods for assessing cell proliferation in 3D scaffolds rely on changes in metabolic activity or total DNA, however, direct quantification of cell number in 3D scaffolds remains a challenge. To address this issue, we developed an unbiased stereology approach that uses systematic-random sampling and thin focal-plane optical sectioning of the scaffolds followed by estimation of total cell number (StereoCount). This approach was validated against an indirect method for measuring the total DNA (DNA content); and the Bürker counting chamber, the current reference method for quantifying cell number. We assessed the total cell number for cell seeding density (cells per unit volume) across four values and compared the methods in terms of accuracy, ease-of-use and time demands. The accuracy of StereoCount markedly outperformed the DNA content for cases with ~ 10,000 and ~ 125,000 cells/scaffold. For cases with ~ 250,000 and ~ 375,000 cells/scaffold both StereoCount and DNA content showed lower accuracy than the Bürker but did not differ from each other. In terms of ease-of-use, there was a strong advantage for the StereoCount due to output in terms of absolute cell numbers along with the possibility for an overview of cell distribution and future use of automation for high throughput analysis. Taking together, the StereoCount method is an efficient approach for direct cell quantification in 3D collagen scaffolds. Its major benefit is that automated StereoCount could accelerate research using 3D scaffolds focused on drug discovery for a wide variety of human diseases.

A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting.

The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions for medical image analysis. Furthermore, accurate stereological quantification of microscopic structures in stained tissue sections plays a critical role in understanding human diseases and developing safe and effective treatments. In this article, we review the most recent deep learning approaches for cell (nuclei) detection and segmentation in cancer and Alzheimer's disease with an emphasis on deep learning approaches combined with unbiased stereology. Major challenges include accurate and reproducible cell detection and segmentation of microscopic images from stained sections. Finally, we discuss potential improvements and future trends in deep learning applied to cell detection and segmentation.

A disector-based framework for the automatic optical fractionator.

Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep learning (DL) offers the possibility of improving throughput by automating the collection of stereology data. We have recently shown that DL can effectively achieve comparable accuracy to manual stereology but with higher repeatability, improved throughput, and less variation due to human factors by quantifying the total number of immunostained cells at their maximal profile of focus in extended depth of field (EDF) images. In the first of two novel contributions in this work, we propose a semi-automatic approach using a handcrafted Adaptive Segmentation Algorithm (ASA) to automatically generate ground truth on EDF images for training our deep learning (DL) models to automatically count cells using unbiased stereology methods. This update increases the amount of training data, thereby improving the accuracy and efficiency of automatic cell counting methods, without a requirement for extra expert time. The second contribution of this work is a Multi-channel Input and Multi-channel Output (MIMO) method using a U-Net deep learning architecture for automatic cell counting in a stack of z-axis images (also known as disector stacks). This DL-based digital automation of the ordinary optical fractionator ensures accurate counts through spatial separation of stained cells in the z-plane, thereby avoiding false negatives from overlapping cells in EDF images without the shortcomings of 3D and recurrent DL models. The contribution overcomes the issue of under-counting errors with EDF images due to overlapping cells in the z-plane (masking). We demonstrate the practical applications of these advances with automatic disector-based estimates of the total number of NeuN-immunostained neurons in a mouse neocortex. In summary, this work provides the first demonstration of automatic estimation of a total cell number in tissue sections using a combination of deep learning and the disector-based optical fractionator method.

Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation.

Artificial Intelligence (AI)-based methods allow for automatic assessment of pain intensity based on continuous monitoring and processing of subtle changes in sensory signals, including facial expression, body movements, and crying frequency. Currently, there is a large and growing need for expanding current AI-based approaches to the assessment of postoperative pain in the neonatal intensive care unit (NICU). In contrast to acute procedural pain in the clinic, the NICU has neonates emerging from postoperative sedation, usually intubated, and with variable energy reserves for manifesting forceful pain responses. Here, we present a novel multi-modal approach designed, developed, and validated for assessment of neonatal postoperative pain in the challenging NICU setting. Our approach includes a robust network capable of efficient reconstruction of missing modalities (e.g., obscured facial expression due to intubation) using an unsupervised spatio-temporal feature learning with a generative model for learning the joint features. Our approach generates the final pain score along with the intensity using an attentional cross-modal feature fusion. Using experimental dataset from postoperative neonates in the NICU, our pain assessment approach achieves superior performance (AUC 0.906, accuracy 0.820) as compared to the state-of-the-art approaches.

Stereological Changes in Microvascular Parameters in Hippocampus of a Transgenic Rat Model of Alzheimer's Disease.

BACKGROUND: Microcirculatory factors play an important role in amyloid-ß (Aß)-related neuropathology in Alzheimer's disease (AD). Transgenic (Tg) rat models of mutant Aß deposition can enhance our understanding of this microvascular pathology. OBJECTIVE: Here we report stereology-based quantification and comparisons (between- and within-group) of microvessel length and number and associated parameters in hippocampal subregions in Tg model of AD in Fischer 344 rats and non-Tg littermates. METHODS: Systematic-random samples of tissue sections were processed and laminin immunostained to visualize microvessels through the entire hippocampus in Tg and non-Tg rats. A computer-assisted stereology system was used to quantify microvessel parameters including total number, total length, and associated densities in dentate gyrus (DG) and cornu ammonis (CA) subregions. RESULTS: Thin hair-like capillaries are common near Aß plaques in hippocampal subregions of Tg rats. There are a 53% significant increase in average length per capillary across entire hippocampus (p≤0.04) in Tg compared to non-Tg rats; 49% reduction in capillary length in DG (p≤0.02); and, higher microvessel density in principal cell layers (p≤0.03). Furthermore, within-group comparisons confirm Tg but not non-Tg rats have significant increase in number density (p≤0.01) and potential diffusion distance (p≤0.04) of microvessels in principal cell layers of hippocampal subregions. CONCLUSION: We show the Tg deposition of human Aß mutations in rats disrupts the wild-type microanatomy of hippocampal microvessels. Stereology-based microvascular parameters could promote the development of novel strategies for protection and the therapeutic management of AD.

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise and more efficient automatic approaches for quantifying morphometric changes in biological tissue, we have shown that both deep learning-based and hand-crafted algorithms can estimate the total number of histologically stained cells at their maximal profile of focus in Extended Depth of Field (EDF) images. Deep learning-based approaches show accuracy comparable to manual counts on EDF images but significant enhancement in reproducibility, throughput efficiency and reduced error from human factors. However, a majority of the automated counts are designed for single-immunostained tissue sections. NEW METHOD: To expand the automatic counting methods to more complex dual-staining protocols, we developed an adaptive method to separate stain color channels on images from tissue sections stained by a primary immunostain with secondary counterstain. COMPARISON WITH EXISTING METHODS: The proposed method overcomes the limitations of the state-of-the-art stain-separation methods, like the requirement of pure stain color basis as a prerequisite or stain color basis learning on each image. RESULTS: Experimental results are presented for automatic counts using deep learning-based and hand-crafted algorithms for sections immunostained for neurons (Neu-N) or microglial cells (Iba-1) with cresyl violet counterstain. CONCLUSION: Our findings show more accurate counts by deep learning methods compared to the handcrafted method. Thus, stain-separated images can function as input for automatic deep learning-based quantification methods designed for single-stained tissue sections.

Future roles of artificial intelligence in early pain management of newborns.

The advent of increasingly sophisticated medical technology, surgical interventions, and supportive healthcare measures is raising survival probabilities for babies born premature and/or with life-threatening health conditions. In the United States, this trend is associated with greater numbers of neonatal surgeries and higher admission rates into neonatal intensive care units (NICU) for newborns at all birth weights. Following surgery, current pain management in NICU relies primarily on narcotics (opioids) such as morphine and fentanyl (about 100 times more potent than morphine) that lead to a number of complications, including prolonged stays in NICU for opioid withdrawal. In this paper, we review current practices and challenges for pain assessment and treatment in NICU and outline ongoing efforts using Artificial Intelligence (AI) to support pain- and opioid-sparing approaches for newborns in the future. A major focus for these next-generation approaches to NICU-based pain management is proactive pain mitigation (avoidance) aimed at preventing harm to neonates from both postsurgical pain and opioid withdrawal. AI-based frameworks can use single or multiple combinations of continuous objective variables, that is, facial and body movements, crying frequencies, and physiological data (vital signs), to make high-confidence predictions about time-to-pain onset following postsurgical sedation. Such predictions would create a therapeutic window prior to pain onset for mitigation with non-narcotic pharmaceutical and nonpharmaceutical interventions. These emerging AI-based strategies have the potential to minimize or avoid damage to the neonate's body and psyche from postsurgical pain and opioid withdrawal.

Low doses of Bisphenol S affect post-translational modifications of sperm proteins in male mice.

BACKGROUND: Bisphenol S (BPS) is increasingly used as a replacement for bisphenol A in the manufacture of products containing polycarbonates and epoxy resins. However, further studies of BPS exposure are needed for the assessment of health risks to humans. In this study we assessed the potential harmfulness of low-dose BPS on reproduction in male mice. METHODS: To simulate human exposure under experimental conditions, 8-week-old outbred ICR male mice received 8 weeks of drinking water containing a broad range of BPS doses [0.001, 1.0, or 100 µg/kg body weight (bw)/day, BPS1-3] or vehicle control. Mice were sacrificed and testicular tissue taken for histological analysis and protein identification by nano-liquid chromatography/mass spectrometry (MS) and sperm collected for immunodetection of acetylated lysine and phosphorylated tyrosine followed by protein characterisation using matrix-assisted laser desorption ionisation time-of-flight MS (MALDI-TOF MS). RESULTS: The results indicate that compared to vehicle, 100 µg/kg/day exposure (BPS3) leads to 1) significant histopathology in testicular tissue; and, 2) higher levels of the histone protein γH2AX, a reliable marker of DNA damage. There were fewer mature spermatozoa in the germ layer in the experimental group treated with 1 µg/kg bw (BPS2). Finally, western blot and MALDI-TOF MS studies showed significant alterations in the sperm acetylome and phosphorylome in mice treated with the lowest exposure (0.001 µg/kg/day; BPS1), although the dose is several times lower than what has been published so far. CONCLUSIONS: In summary, this range of qualitative and quantitative findings in young male mice raise the possibility that very low doses of BPS may impair mammalian reproduction through epigenetic modifications of sperm proteins.

No Evidence of Neurogenesis in Adult Rat Sympathetic Ganglia Following Guanethidine-Induced Neuronal Loss.

The potential for neurogenesis in the cranial (superior) cervical ganglia (SCG) of the sympathetic nervous system was evaluated. Eleven consecutive daily doses of guanethidine (100 mg/kg/d) were administered intraperitoneally to rats in order to destroy postganglionic sympathetic neurons in SCG. Following the last dose, animals were allowed to recover 1, 3, or 6 months. Right and left SCG from guanethidine-treated and age-matched, vehicle-treated control rats were harvested for histopathologic, morphometric, and stereologic evaluations. Both morphometric and stereologic evaluations confirmed neuron loss following guanethidine treatment. Morphometric analysis revealed a 50% to 60% lower number of tyrosine hydroxylase (TH)-positive neurons per unit area of SCG at both 3 and 6 months of recovery, compared to ganglia of age-matched controls, with no evidence of restoration of neuron density between 3 and 6 months. Reductions in TH-positive neurons following guanethidine treatment were corroborated by unbiased stereology of total hematoxylin and eosin-stained neuron numbers in SCG. Stereologic analyses revealed that total neuron counts were lower by 37% at 3 months of recovery when compared to age-matched vehicle controls, again with no obvious restoration between 3 and 6 months. Thus, no evidence was found that postganglionic neurons of the sympathetic nervous system in the adult rat have a neurogenic capacity.

Age-Related Decrease in Tyrosine Hydroxylase Immunoreactivity in the Substantia Nigra and Region-Specific Changes in Microglia Morphology in HIV-1 Tg Rats.

Animal models have been used to study cellular processes related to human immunodeficiency virus-1 (HIV-1)-associated neurocognitive disorders (HAND). The HIV-1 transgenic (Tg) rat expresses HIV viral genes except the gag-pol replication genes and exhibits neuropathological features similar to HIV patients receiving combined antiretroviral therapy (cART). Using this rat, alterations in dopaminergic function have been demonstrated; however, the data for neuroinflammation and glial reactivity is conflicting. Differences in behavior, tyrosine hydroxylase (TH) immunoreactivity, neuroinflammation, and glia reactivity were assessed in HIV-1 Tg male rats. At 6 and 12 weeks of age, rotarod performance was diminished, motor activity was not altered, and active avoidance latency performance and memory were diminished in HIV-1 Tg rats. TH+ immunoreactivity in the substantia nigra (SN) was decreased at 8 months but not at 2-5 months. At 5 months, astrocyte and microglia morphology was not altered in the cortex, hippocampus, or SN. In the striatum, astrocytes were unaltered, microglia displayed slightly thickened proximal processes, mRNA levels for Iba1 and Cd11b were elevated, and interleukin (Il)1α,Cxcr3, and cell adhesion molecule, Icam, decreased. In the hippocampus, mRNA levels for Tnfa and Cd11b were slightly elevated. No changes were observed in the cortex or SN. The data support an age-related effect of HIV proteins upon the nigrostriatal dopaminergic system and suggest an early response of microglia in the terminal synaptic region with little evidence of an associated neuroinflammatory response across brain regions.

Reduced Hippocampal Dendrite Branching, Spine Density and Neurocognitive Function in Premature Rabbits, and Reversal with Estrogen or TrkB Agonist Treatment.

Preterm-born children suffer from neurological and behavioral disorders. Herein, we hypothesized that premature birth and non-maternal care of preterm newborns might disrupt neurobehavioral function, hippocampal dendritic arborization, and dendritic spine density. Additionally, we assessed whether 17ß-estradiol (E2) replacement or the TrkB receptor agonist, 7,8-dihydroxyflavone (DHF), would reverse compromised dendritic development and cognitive function in preterm newborns. These hypotheses were tested by comparing preterm (E28.5) rabbit kits cared and gavage-fed by laboratory personnel and term-kits reared and breast-fed by their mother doe at an equivalent postconceptional age. Neurobehavioral tests showed that both premature-birth and formula-feeding with non-maternal care led to increased anxiety behavior, poor social interaction, and lack of novelty preference compared with term-kits. Dendritic branching and number of total or mushroom dendritic spines were reduced in the CA1 field of preterm-kits compared with term controls. While CDC42 and Rac1/2/3 expression levels were lower, RhoA-activity was higher in preterm-kits compared with term controls. Both E2 and DHF treatment reversed prematurity-induced reduction in spine density, reduced total RhoA-GTPase levels, and enhanced cognitive function. Hence, prematurity and non-maternal care result in cognitive deficits, and reduced dendritic arbors and spines in CA1. E2 replacement or DHF treatment might reverse changes in dendritic spines and improve neurodevelopment in premature infants.

Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex.

Collection of unbiased stereology data currently relies on relatively simple, low throughput technology developed in the mid-1990s. In an effort to improve the accuracy and efficiency of these integrated hardware-software-digital microscopy systems, we have developed an automatic segmentation algorithm (ASA) for automatic stereology counts using the unbiased optical fractionator method. Here we report on a series of validation experiments in which immunostained neurons (NeuN) and microglia (Iba1) were automatically counted in tissue sections through a mouse neocortex. In the first step, a minimum of 100 systematic-random z-axis image stacks (disector stacks) containing NeuN- and Iba1-immunostained cells were automatically collected using a software-controlled 3 axes (XYZ) stage motor. In the second step, each disector stack was converted to an extended depth of field (EDF) image in which each cell is shown at its optimal plane of focus. Third, individual neurons and microglia were segmented and the regional minimas were extracted and used as seed regions for cells in a watershed segmentation algorithm. Finally, the unbiased disector frame and counting rules were used to make unbiased parameter estimates for neurons and microglia cells. The results for both NeuN neurons and Iba1 microglia were compared to manual counts made by a moderately experienced data collector from the same disector stacks. The final results show lower error rates for counts of Iba1-immunostained microglia cells than for quantifying NeuN-immunostained neurons, most likely due to less three-dimensional overlapping of Iba1 cells. We report that the throughput efficiency of using ASA to automatically annotate images of Iba1 microglia is more than five times greater than that of manual stereology counts of the same sections. Moreover, we show that ASA is significantly more accurate in counting microglia cells than a moderately experienced data collector (about 10% higher overall accuracy) when both were compared to counts by an expert neurohistologist. Thus, the ASA method applied to EDF images from disector stacks can be extremely useful to automate and increase the accuracy of cell counts, which could be especially helpful and cost-effective when expert help is not available. Another potential use of our ASA approach is to generate unsupervised ground truth as an efficient alternative to manual annotation for training deep learning models, as shown in our ongoing work.

Automatic stereology of mean nuclear size of neurons using an active contour framework.

The use of unbiased stereology to quantify structural parameters such as mean cell and nuclear size (area and volume) can be useful for a wide variety of biological studies. Here we propose a novel segmentation framework using an Active Contour Model to automate the collection of stereology from stained cells and other objects in tissue sections. This approach is demonstrated for stained brain sections from young adult Fischer 344 rats. Animals were perfused in-vivo with 4% paraformaldehyde and sectioned by frozen microtomy at an instrument setting of 40 µm. For each rat brain, a systematic-random set of sections through the entire substantia nigra pars compacta (SN) were immunostained to reveal tyrosine hydroxylase (TH)-immunopositive neurons. The novel framework applied an active contour (modified balloon snake) model with non-constant balloon force to automatically segment and quantify neuronal cell bodies by stereological point counting (SPC). Several contours were initialized in the image and based on the contour fit after 200 iterations classified as immunopositive (signal) or background contours in a sequential manner. Cell contours were determined in four steps based on several criteria, e.g., area of contour, dispersion measure, and degree of overlap. The image was automatically segmented according to the final contours. Using a point grid automatically generated at systematic-random orientations over the images, points hitting the segmented neural cell bodies were automatically counted. The final values from the automatic framework were compared with findings for ground truth (manual SPC). The results of this study show a strong agreement between data collected by the automatic framework and the ground truth (R2 ≥ 0.95) with a 5× gain in time efficiency for the automatic SPC. These findings give strong support for future applications of pattern recognition for assessing stereological parameters of biological objects identified by high signal:noise stains.

Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology.

In recent decades stereology-based studies have played a significant role in understanding brain aging and developing novel drug discovery strategies for treatment of neurological disease and mental illness. A major obstacle to further progress in a wide range of neuroscience sub-disciplines remains the lack of high-throughput technology for stereology analyses. Though founded on methodologically unbiased principles, commercially available stereology systems still rely on well-trained humans to manually count hundreds of cells within each region of interest (ROI). Even for a simple study with 10 controls and 10 treated animals, cell counts typically require over a month of tedious labor and high costs. Furthermore, these studies are prone to errors and poor reproducibility due to human factors such as subjectivity, variable training, recognition bias, and fatigue. Here we propose a deep neural network-stereology combination to automatically segment and estimate the total number of immunostained neurons on tissue sections. Our three-step approach consists of (1) creating extended-depth-of-field (EDF) images from z-stacks of images (disector stacks); (2) applying an adaptive segmentation algorithm (ASA) to label stained cells in the EDF images (i.e., create masks) for training a convolutional neural network (CNN); and (3) use the trained CNN model to automatically segment and count the total number of cells in test disector stacks using the optical fractionator method. The automated stereology approach shows less than 2% error and over 5× greater efficiency compared to counts by a trained human, without the subjectivity, tedium, and poor precision associated with conventional stereology.

A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images.

We propose a framework to detect and segment nuclei and segment overlapping cytoplasm in cervical cytology images. This is a challenging task due to folded cervical cells with spurious edges, poor contrast of cytoplasm and presence of neutrophils and artifacts. The algorithm segments nuclei and cell clumps in extended depth of field (EDF) images and uses volume images to segment overlapping cytoplasm. The boundaries are first approximated by a defined similarity metric and are refined in two steps by reducing concavity, iterative smoothing and outliers removal. We evaluated our framework on two public datasets provided in the first and second overlapping cervical cell segmentation challenges (ISBI 2014 and 2015). The results show that our method outperforms other state-of-the-art algorithms on both datasets. The results on the ISBI 2014 dataset show that our method missed less than 5% of cells when the pairwise cell overlapping degree was not higher than 0.3 and it missed only 7% of cells on average in a dataset of 810 synthetic images with 4860 (overlapping) cells. On the same dataset, it outperforms other state-of-the-art methods in nucleus detection with precision 0.961 and recall 0.933. The results on the ISBI 2015 dataset containing real cervical EDF images show that our method misses around 20% of cells in EDF images where a segmentation is considered a miss if it has dice similarity coefficient not greater than 0.7. The 20% miss rate is around half of the miss rate of two other recent methods.

Unbiased estimation of cell number using the automatic optical fractionator.

A novel stereology approach, the automatic optical fractionator, is presented for obtaining unbiased and efficient estimates of the number of cells in tissue sections. Used in combination with existing segmentation algorithms and ordinary immunostaining methods, automatic estimates of cell number are obtainable from extended depth of field images built from three-dimensional volumes of tissue (disector stacks). The automatic optical fractionator is more accurate, 100% objective and 8-10 times faster than the manual optical fractionator. An example of the automatic fractionator is provided for counts of immunostained neurons in neocortex of a genetically modified mouse model of neurodegeneration. Evidence is presented for the often overlooked prerequisite that accurate counting by the optical fractionator requires a thin focal plane generated by a high optical resolution lens.