Cell segmentation
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OBJECTIVES: This article focuses on the detection of cells in low-contrast brightfield microscopy images; in our case, it is chronic lymphocytic leukaemia cells. The automatic detection of cells from brightfield time-lapse microscopic images brings new opportunities in cell morphology and migration studies; to achieve the desired results, it is advisable to use state-of-the-art image segmentation methods that not only detect the cell but also detect its boundaries with the highest possible accuracy, thus defining its shape and dimensions. METHODS: We compared eight state-of-the-art neural network architectures with different backbone encoders for image data segmentation, namely U-net, U-net++, the Pyramid Attention Network, the Multi-Attention Network, LinkNet, the Feature Pyramid Network, DeepLabV3, and DeepLabV3+. The training process involved training each of these networks for 1000 epochs using the PyTorch and PyTorch Lightning libraries. For instance segmentation, the watershed algorithm and three-class image semantic segmentation were used. We also used StarDist, a deep learning-based tool for object detection with star-convex shapes. RESULTS: The optimal combination for semantic segmentation was the U-net++ architecture with a ResNeSt-269 background with a data set intersection over a union score of 0.8902. For the cell characteristics examined (area, circularity, solidity, perimeter, radius, and shape index), the difference in mean value using different chronic lymphocytic leukaemia cell segmentation approaches appeared to be statistically significant (Mann-Whitney U test, P < .0001). CONCLUSION: We found that overall, the algorithms demonstrate equal agreement with ground truth, but with the comparison, it can be seen that the different approaches prefer different morphological features of the cells. Consequently, choosing the most suitable method for instance-based cell segmentation depends on the particular application, namely, the specific cellular traits being investigated.
- Klíčová slova
- Cell detection, U-net++, cell segmentation, chronic lymphocytic leukaemia cells, image analysis,
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. RESULTS: We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. CONCLUSIONS: We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.
- Klíčová slova
- Cell segmentation, Differential contrast image, Image reconstruction, Laplacian of Gaussians, Methods comparison, Microscopy, Quantitative phase imaging,
- MeSH
- algoritmy MeSH
- frakcionace buněk metody MeSH
- lidé MeSH
- mikroskopie metody MeSH
- počítačové zpracování obrazu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Segmentation is one of the most important steps in microscopy image analysis. Unfortunately, most of the methods use fluorescence images for this task, which is not suitable for analysis that requires a knowledge of area occupied by cells and an experimental design that does not allow necessary labeling. In this protocol, we present a simple method, based on edge detection and morphological operations, that separates total area occupied by cells from the background using only brightfield channel image. The resulting segmented picture can be further used as a mask for fluorescence quantification and other analyses. The whole procedure is carried out in open source software Fiji.
- Klíčová slova
- Fiji, ImageJ, brightfield segmentation, cells, image analysis, microscopy,
- Publikační typ
- časopisecké články MeSH
Living cell segmentation from bright-field light microscopy images is challenging due to the image complexity and temporal changes in the living cells. Recently developed deep learning (DL)-based methods became popular in medical and microscopy image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, U-Net-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy. To find the most suitable architecture for our datasets, a residual attention U-Net was proposed and compared with an attention and a simple U-Net architecture. The attention mechanism highlights the remarkable features and suppresses activations in the irrelevant image regions. The residual mechanism overcomes with vanishing gradient problem. The Mean-IoU score for our datasets reaches 0.9505, 0.9524, and 0.9530 for the simple, attention, and residual attention U-Net, respectively. The most accurate semantic segmentation results was achieved in the Mean-IoU and Dice metrics by applying the residual and attention mechanisms together. The watershed method applied to this best - Residual Attention - semantic segmentation result gave the segmentation with the specific information for each cell.
- Klíčová slova
- Cell detection, Deep learning, Microscopy image segmentation, Neural network, Semantic segmentation, Tissue segmentation, Watershed segmentation,
- MeSH
- benchmarking MeSH
- mikroskopie * MeSH
- neuronové sítě MeSH
- počítačové zpracování obrazu * metody MeSH
- sémantika MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The nuclear architecture of selected chromosomes in apoptotic nuclei of human leukemic cells K-562 and HL-60 was investigated. Etoposide and prolonged confluence were used for the induction of apoptosis. DAPI as well as TUNEL labeling of apoptotic nuclear bodies was combined with visualization of chromosomal territories by the FISH technique. Simultaneous vital staining by annexin V, propidium iodide, and Hoechst 33342 was applied to distinguish apoptotic, necrotic, and intact cell fraction of tested populations. Our FISH analyses revealed that the three-dimensional (3D) structure of apoptotic nuclei as well as the 3D structure of apoptotic bodies is preserved in formaldehyde-fixed cells. High-molecular-weight DNA fragmentation was determined in apoptotic K-562 cells in contrast to oligonucleosomal cleavage observed in apoptotic HL-60 cells. In K-562 populations, chromosomal territories were located separately either in one apoptotic body or underwent disassembly into chromosomal segments dispersed into single and/or several apoptotic bodies. The apoptotic disorganization of chromosomal territories was irregular, leading mainly to chromosomal segments of different sizes and, consequently, chromosomal disassembly was not observed at specific sites. In comparison with the control, an increased number of centromeric FISH signals were observed in prolonged confluence-treated K-562 cells induced to apoptosis. This finding can be explained either as a consequence of apoptosis or by polyploidization. Sequential staining of the same apoptotic nuclei by the FISH and TUNEL techniques revealed that chromosomal territory segmentation precedes the formation of nuclear apoptotic bodies.
In this paper, a novel U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance from 0.67 to 0.70 of object-wise intersection over union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining.
- Publikační typ
- časopisecké články MeSH
Biocompatibility testing of new materials is often performed in vitro by measuring the growth rate of mammalian cancer cells in time-lapse images acquired by phase contrast microscopes. The growth rate is measured by tracking cell coverage, which requires an accurate automatic segmentation method. However, cancer cells have irregular shapes that change over time, the mottled background pattern is partially visible through the cells and the images contain artifacts such as halos. We developed a novel algorithm for cell segmentation that copes with the mentioned challenges. It is based on temporal differences of consecutive images and a combination of thresholding, blurring, and morphological operations. We tested the algorithm on images of four cell types acquired by two different microscopes, evaluated the precision of segmentation against manual segmentation performed by a human operator, and finally provided comparison with other freely available methods. We propose a new, fully automated method for measuring the cell growth rate based on fitting a coverage curve with the Verhulst population model. The algorithm is fast and shows accuracy comparable with manual segmentation. Most notably it can correctly separate live from dead cells.
- Klíčová slova
- biocompatibility assessment, cytotoxicity testing, phase contrast microscopy, segmentation, time-lapse,
- MeSH
- algoritmy MeSH
- artefakty MeSH
- časosběrné zobrazování * MeSH
- cytologické techniky přístrojové vybavení metody MeSH
- lidé MeSH
- mikroskopie * MeSH
- rozpoznávání automatizované MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
We present a fast and robust approach to tracking the evolving shape of whole fluorescent cells in time-lapse series. The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the cell boundaries are detected by minimizing the Chan-Vese model in the fast level set-like and graph cut frameworks. To allow simultaneous tracking of multiple cells over time, both frameworks have been integrated with a topological prior exploiting the object indication function. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively.
- MeSH
- buněčné jádro chemie MeSH
- buněčný tracking metody MeSH
- fluorescenční mikroskopie metody MeSH
- krysa rodu Rattus MeSH
- lidé MeSH
- mezenchymální kmenové buňky cytologie MeSH
- nádorové buněčné linie MeSH
- počítačové zpracování obrazu metody MeSH
- tvar buňky fyziologie MeSH
- zvířata MeSH
- Check Tag
- krysa rodu Rattus MeSH
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
This study aims to develop a fully automated imaging protocol independent system for pituitary adenoma segmentation from magnetic resonance imaging (MRI) scans that can work without user interaction and evaluate its accuracy and utility for clinical applications. We trained two independent artificial neural networks on MRI scans of 394 patients. The scans were acquired according to various imaging protocols over the course of 11 years on 1.5T and 3T MRI systems. The segmentation model assigned a class label to each input pixel (pituitary adenoma, internal carotid artery, normal pituitary gland, background). The slice segmentation model classified slices as clinically relevant (structures of interest in slice) or irrelevant (anterior or posterior to sella turcica). We used MRI data of another 99 patients to evaluate the performance of the model during training. We validated the model on a prospective cohort of 28 patients, Dice coefficients of 0.910, 0.719, and 0.240 for tumour, internal carotid artery, and normal gland labels, respectively, were achieved. The slice selection model achieved 82.5% accuracy, 88.7% sensitivity, 76.7% specificity, and an AUC of 0.904. A human expert rated 71.4% of the segmentation results as accurate, 21.4% as slightly inaccurate, and 7.1% as coarsely inaccurate. Our model achieved good results comparable with recent works of other authors on the largest dataset to date and generalized well for various imaging protocols. We discussed future clinical applications, and their considerations. Models and frameworks for clinical use have yet to be developed and evaluated.
- Klíčová slova
- Image segmentation, Machine learning, Magnetic resonance imaging, Pituitary adenoma,
- MeSH
- adenom * diagnostické zobrazování chirurgie MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- nádory hypofýzy * diagnostické zobrazování chirurgie MeSH
- neuronové sítě MeSH
- počítačové zpracování obrazu metody MeSH
- prospektivní studie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVE: The objective of this study was to develop a deep learning model for automated pituitary adenoma segmentation in MRI scans for stereotactic radiosurgery planning and to assess its accuracy and efficiency in clinical settings. METHODS: An nnU-Net-based model was trained on MRI scans with expert segmentations of 582 patients treated with Leksell Gamma Knife over the course of 12 years. The accuracy of the model was evaluated by a human expert on a separate dataset of 146 previously unseen patients. The primary outcome was the comparison of expert ratings between the predicted segmentations and a control group consisting of original manual segmentations. Secondary outcomes were the influence of tumor volume, previous surgery, previous stereotactic radiosurgery (SRS), and endocrinological status on expert ratings, performance in a subgroup of nonfunctioning macroadenomas (measuring 1000-4000 mm3) without previous surgery and/or radiosurgery, and influence of using additional MRI modalities as model input and time cost reduction. RESULTS: The model achieved Dice similarity coefficients of 82.3%, 63.9%, and 79.6% for tumor, normal gland, and optic nerve, respectively. A human expert rated 20.6% of the segmentations as applicable in treatment planning without any modifications, 52.7% as applicable with minor manual modifications, and 26.7% as inapplicable. The ratings for predicted segmentations were lower than for the control group of original segmentations (p < 0.001). Larger tumor volume, history of a previous radiosurgery, and nonfunctioning pituitary adenoma were associated with better expert ratings (p = 0.005, p = 0.007, and p < 0.001, respectively). In the subgroup without previous surgery, although expert ratings were more favorable, the association did not reach statistical significance (p = 0.074). In the subgroup of noncomplex cases (n = 9), 55.6% of the segmentations were rated as applicable without any manual modifications and no segmentations were rated as inapplicable. Manually improving inaccurate segmentations instead of creating them from scratch led to 53.6% reduction of the time cost (p < 0.001). CONCLUSIONS: The results were applicable for treatment planning with either no or minor manual modifications, demonstrating a significant increase in the efficiency of the planning process. The predicted segmentations can be loaded into the planning software used in clinical practice for treatment planning. The authors discuss some considerations of the clinical utility of the automated segmentation models, as well as their integration within established clinical workflows, and outline directions for future research.
- Klíčová slova
- Leksell Gamma Knife, automated segmentation, machine learning, pituitary adenoma, pituitary surgery, stereotactic radiosurgery,
- MeSH
- adenom * diagnostické zobrazování radioterapie chirurgie MeSH
- deep learning * MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- magnetická rezonanční tomografie metody MeSH
- nádory hypofýzy * diagnostické zobrazování radioterapie chirurgie MeSH
- radiochirurgie * metody MeSH
- senioři MeSH
- umělá inteligence * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
