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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.
Based on the orientation of the osteons, the basal portion and the alveolar portion of the body of the human mandible can be distinguished. In the compact bone, two types of microporosities can be quantified, the osteocyte lacunae and the vascular canals. Our aim was (i) to perform three-dimensional reconstruction of osteocyte lacunae to suggest an efficient means of sampling to estimate their numerical density and (ii) to compare bone microporosities in the basal and the alveolar portions of ten mandibles. Using optical disector, we estimated the density of osteocyte lacunae, and using a stereological point-counting technique, we quantified the area fraction of the vascular canals. The diameter of the lacunae was 14±3μm. While the fraction of vascular canals was comparable in both parts of the body of the mandible, the numerical density of osteocyte lacunae was higher (p=0.007) in the alveolar portion (17056±1264/mm(3)) than in the basal portion (14522±665/mm(3)). The lacunar and vascular microporosities were statistically independent of each other. As this is the first three-dimensional counting of osteocyte lacunae, we discuss the relation of this parameter to the biomechanics of the mandible, and we compare our data with previously used two-dimensional methods. We present an efficient sampling method that is useful for the histological description of bone microporosities. When taking into account the spatial characteristics of lacunae, the locally specific numerical density of lacunae can be easily assessed with the three-dimensional counting method, which is not biased by the variation in size and orientation of the lacunae.
- MeSH
- anatomické modely MeSH
- lidé středního věku MeSH
- lidé MeSH
- mandibula cytologie MeSH
- mikroskopie metody MeSH
- osteocyty cytologie MeSH
- počítačová simulace MeSH
- poréznost MeSH
- senioři MeSH
- zobrazování trojrozměrné metody MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Chloroplast number per cell is a frequently examined quantitative anatomical parameter, often estimated by counting chloroplast profiles in two-dimensional (2D) sections of mesophyll cells. However, a mesophyll cell is a three-dimensional (3D) structure and this has to be taken into account when quantifying its internal structure. We compared 2D and 3D approaches to chloroplast counting from different points of view: (i) in practical measurements of mesophyll cells of Norway spruce needles, (ii) in a 3D model of a mesophyll cell with chloroplasts, and (iii) using a theoretical analysis. We applied, for the first time, the stereological method of an optical disector based on counting chloroplasts in stacks of spruce needle optical cross-sections acquired by confocal laser-scanning microscopy. This estimate was compared with counting chloroplast profiles in 2D sections from the same stacks of sections. Comparing practical measurements of mesophyll cells, calculations performed in a 3D model of a cell with chloroplasts as well as a theoretical analysis showed that the 2D approach yielded biased results, while the underestimation could be up to 10-fold. We proved that the frequently used method for counting chloroplasts in a mesophyll cell by counting their profiles in 2D sections did not give correct results. We concluded that the present disector method can be efficiently used for unbiased estimation of chloroplast number per mesophyll cell. This should be the method of choice, especially in coniferous needles and leaves with mesophyll cells with lignified cell walls where maceration methods are difficult or impossible to use.
The porcine liver is frequently used as a large animal model for verification of surgical techniques, as well as experimental therapies. Often, a histological evaluation is required that include measurements of the size, nuclearity or density of hepatocytes. Our aims were to assess the mean number-weighted volume of hepatocytes, the numerical density of hepatocytes, and the fraction of binuclear hepatocytes (BnHEP) in the porcine liver, and compare the distribution of these parameters among hepatic lobes and macroscopic regions of interest (ROIs) with different positions related to the liver vasculature. Using disector and nucleator as design-based stereological methods, the morphometry of hepatocytes was quantified in seven healthy piglets. The samples were obtained from all six hepatic lobes and three ROIs (peripheral, paracaval and paraportal) within each lobe. Histological sections (thickness 16 μm) of formalin-fixed paraffin-embedded material were stained with the periodic acid-Schiff reaction to indicate the cell outlines and were assessed in a series of 3-μm-thick optical sections. The mean number-weighted volume of mononuclear hepatocytes (MnHEP) in all samples was 3670 ± 805 μm(3) (mean ± SD). The mean number-weighted volume of BnHEP was 7050 ± 2550 μm(3) . The fraction of BnHEP was 4 ± 2%. The numerical density of all hepatocytes was 146 997 ± 15 738 cells mm(-3) of liver parenchyma. The porcine hepatic lobes contained hepatocytes of a comparable size, nuclearity and density. No significant differences were identified between the lobes. The peripheral ROIs of the hepatic lobes contained the largest MnHEP with the smallest numerical density. The distribution of a larger MnHEP was correlated with a larger volume of BnHEP and a smaller numerical density of all hepatocytes. Practical recommendations for designing studies that involve stereological evaluations of the size, nuclearity and density of hepatocytes in porcine liver are provided.
- MeSH
- hepatocyty * MeSH
- játra anatomie a histologie cytologie MeSH
- počet buněk metody MeSH
- prasata MeSH
- velikost buňky * MeSH
- zvířata MeSH
- Check Tag
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
To provide basic data on the local differences in density of microvessels between various parts of the human brain, including representative grey and white matter structures of the cerebral hemispheres, the brain stem and the cerebellum, we quantified the numerical density NV and the length density LV of microvessels in two human brains. We aimed to correlate the density of microvessels with previously published data on their preferential orientation (anisotropy). Microvessels were identified using immunohistochemistry for laminin in 32 samples harvested from the following brain regions of two adult individuals: the cortex of the telencephalon supplied by the anterior, middle, and posterior cerebral artery; the basal ganglia (putamen and globus pallidus); the thalamus; the subcortical white matter of the telencephalon; the internal capsule; the pons; the cerebellar cortex; and the cerebellar white matter. NV was calculated from the number of vascular branching points and their valence, which were assessed using the optical disector in 20-μm-thick sections. LV was estimated using counting frames applied to routine sections with randomized cutting planes. After correction for shrinkage, NV in the cerebral cortex was 1311±326mm-3 (mean±SD) and LV was 255±119mm-2. Similarly, in subcortical grey matter (which included the basal ganglia and thalamus), NV was 1350±445mm-3 and LV was 328±117mm-2. The vascular networks of cortical and subcortical grey matter were comparable. Their densities were greater than in the white matter, with NV=222±147mm-3 and LV=160±96mm-2. NV was moderately correlated with LV. In parts of brain with greater NV, blood vessels lacked a preferential orientation. Our data were in agreement with other studies on microvessel density focused on specific brain regions, but showed a greater variability, thus mapping the basic differences among various parts of brain. To facilitate the planning of other studies on brain vascularity and to support the development of computational models of human brain circulation based on real microvascular morphology; stereological data in form of continuous variables are made available as supplements.
