Cytoplasmic Segment Research Articles

Unsupervised cervical cell instance segmentation method integrating cellular characteristics.

Cell instance segmentation is a key technology for cervical cancer auxiliary diagnosis systems. However, pixel-level annotation is time-consuming and labor-intensive, making it difficult to obtain a large amount of annotated data. This results in the model not being fully trained. In response to these problems, this paper proposes an unsupervised cervical cell instance segmentation method that integrates cell characteristics. Cervical cells have a clear corresponding structure between the nucleus and cytoplasm. This method fully takes this feature into account by building a dual-flow framework to locate the nucleus and cytoplasm and generate high-quality pseudo-labels. In the nucleus segmentation stage, the position and range of the nucleus are determined using the standard cell-restricted nucleus segmentation method. In the cytoplasm segmentation stage, a multi-angle collaborative segmentation method is used to achieve the positioning of the cytoplasm. First, taking advantage of the self-similarity characteristics of pixel blocks in cells, a cytoplasmic segmentation method based on self-similarity map iteration is proposed. The pixel blocks are mapped from the perspective of local details, and the iterative segmentation is repeated. Secondly, using low-level features such as cell color and shape, a self-supervised heatmap-aware cytoplasm segmentation method is proposed to obtain the activation map of the cytoplasm from the perspective of global attention. The two methods are fused to determine cytoplasmic regions, and combined with nuclear locations, high-quality pseudo-labels are generated. These pseudo-labels are used to train the model cyclically, and the loss strategy is used to encourage the model to discover new object masks, thereby obtaining a segmentation model with better performance. Experimental results show that this method achieves good results in cytoplasm segmentation. On the three datasets of ISBI, MS_CellSeg, and Cx22, 54.32%, 44.64%, and 66.52% AJI were obtained, respectively, which is better than other typical unsupervised methods selected in this article.

An improved approach for automated cervical cell segmentation with PointRend

Regular screening for cervical cancer is one of the best tools to reduce cancer incidence. Automated cell segmentation in screening is an essential task because it can present better understanding of the characteristics of cervical cells. The main challenge of cell cytoplasm segmentation is that many boundaries in cell clumps are extremely difficult to be identified. This paper proposes a new convolutional neural network based on Mask RCNN and PointRend module, to segment overlapping cervical cells. The PointRend head concatenates fine grained features and coarse features extracted from different feature maps to fine-tune the candidate boundary pixels of cell cytoplasm, which are crucial for precise cell segmentation. The proposed model achieves a 0.97 DSC (Dice Similarity Coefficient), 0.96 TPRp (Pixelwise True Positive Rate), 0.007 FPRp (Pixelwise False Positive Rate) and 0.006 FNRo (Object False Negative Rate) on dataset from ISBI2014. Specially, the proposed method outperforms state-of-the-art result by about 3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\\%$$\\end{document} on DSC, 1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\\%$$\\end{document} on TPRp and 1.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.4\\%$$\\end{document} on FNRo respectively. The performance metrics of our model on dataset from ISBI2015 are slight better than the average value of other approaches. Those results indicate that the proposed method could be effective in cytological analysis and then help experts correctly discover cervical cell lesions

Automated 3D cytoplasm segmentation in soft X-ray tomography

Cells' structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune Tcells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.

Structural change of the cytoplasmic N-terminus and S1 segment of voltage-sensing phosphatase reported by Anap.

Voltage-sensing phosphatase contains a structurally conserved S1-S4-based voltage-sensor domain, which undergoes a conformational transition in response to membrane potential change. Unlike that of channels, it is functional even in isolation and is therefore advantageous for studying the transition mechanism, but its nature has not yet been fully elucidated. This study aimed to address whether the cytoplasmic N-terminus and S1 exhibit structural change. Anap, an environment-sensitive unnatural fluorescentamino acid, was site-specifically introduced to the voltage sensor domain to probe local structural changes by using oocyte voltage clamp and photometry. Tetramethylrhodamine was also used to probe some extracellularly accessible positions. In total, 51 positions were investigated. We detected robust voltage-dependent signals from widely distributed positions including N-terminus and S1. In addition, response to hyperpolarization was observed at the extracellular end of S1, reflecting the local structure flexibility of the voltage-sensor domain in the down-state. We also found that the mechanical coupling between the voltage-sensor and phosphatase domains affects the depolarization-induced optical signals but not the hyperpolarization-induced signals. These results fill a gap between the previous interpretations from the structural and biophysical approaches and should provide important insights into the mechanisms of the voltage-sensor domain transition as well as its coupling with the effector.

Deciphering the sub-Golgi localization of glycosyltransferases via 3D super-resolution imaging.

The Golgi apparatus, a crucial organelle involved in protein processing, including glycosylation, exhibits complex sub-structures, i.e., cis-, medial, and trans-cisternae. This study investigated the distribution of glycosyltransferases within the Golgi apparatus of mammalian cells via 3D super-resolution imaging. Focusing on human glycosyltransferases involved in N-glycan modification, we found that even enzymes presumed to coexist in the same Golgi compartment exhibit nuanced variations in localization. By artificially making their N-terminal regions [composed of a cytoplasmic, transmembrane, and stem segment (CTS)] identical, it was possible to enhance the degree of their colocalization, suggesting the decisive role of this region in determining the sub-Golgi localization of enzymes. Ultimately, this study reveals the molecular codes within CTS regions as key determinants of glycosyltransferase localization, providing insights into precise control over the positioning of glycosyltransferases, and consequently, the interactions between glycosyltransferases and substrate glycoproteins as cargoes in the secretory pathway. This study advances our understanding of Golgi organization and opens avenues for programming the glycosylation of proteins for clinical applications.Key words: Golgi apparatus, glycosyltransferase, 3D super-resolution imaging, N-glycosylation.

A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images

Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance quantitative analysis and refine diagnostic capabilities, precise segmentation of the cervical cytoplasm and nucleus using deep learning techniques holds immense promise. This research focuses on addressing the primary challenge of achieving accurate segmentation in the presence of noisy data commonly encountered in Pap smear images. Poisson noise, a prevalent type of noise, corrupts these images, impairing the precise delineation of the cytoplasm and nucleus. Consequently, segmentation boundaries become indistinct, leading to compromised overall accuracy. To overcome these limitations, the utilization of U-Net, a deep learning architecture specifically designed for automatic segmentation, has been proposed. This approach aims to mitigate the adverse effects of Poisson noise on the digitized Pap smear slides. The evaluation of the proposed methodology involved a dataset of 110 Pap smear slides. The experimental results demonstrate that the proposed approach successfully achieves precise segmentation of the nucleus and cytoplasm in noise-free images. By preserving the boundaries of both cellular components, the method facilitates accurate feature extraction, thus contributing to improved diagnostic capabilities. Comparative analysis between noisy and noise-free images reveals the superiority of the presented approach in terms of segmentation accuracy, as measured by various metrics, including the Dice coefficient, specificity, sensitivity, and intersection over union (IoU). The findings of this study underline the potential of deep-learning-based segmentation techniques to enhance cervical cancer diagnosis and pave the way for improved quantitative analysis in this critical field of women’s health.

Automated cervical cell segmentation using deep ensemble learning

BackgroundCervical cell segmentation is a fundamental step in automated cervical cancer cytology screening. The aim of this study was to develop and evaluate a deep ensemble model for cervical cell segmentation including both cytoplasm and nucleus segmentation.MethodsThe Cx22 dataset was used to develop the automated cervical cell segmentation algorithm. The U-Net, U-Net + + , DeepLabV3, DeepLabV3Plus, Transunet, and Segformer were used as candidate model architectures, and each of the first four architectures adopted two different encoders choosing from resnet34, resnet50 and denseNet121. Models were trained under two settings: trained from scratch, encoders initialized from ImageNet pre-trained models and then all layers were fine-tuned. For every segmentation task, four models were chosen as base models, and Unweighted average was adopted as the model ensemble method.ResultsU-Net and U-Net + + with resnet34 and denseNet121 encoders trained using transfer learning consistently performed better than other models, so they were chosen as base models. The ensemble model obtained the Dice similarity coefficient, sensitivity, specificity of 0.9535 (95% CI:0.9534–0.9536), 0.9621 (0.9619–0.9622),0.9835 (0.9834–0.9836) and 0.7863 (0.7851–0.7876), 0.9581 (0.9573–0.959), 0.9961 (0.9961–0.9962) on cytoplasm segmentation and nucleus segmentation, respectively. The Dice, sensitivity, specificity of baseline models for cytoplasm segmentation and nucleus segmentation were 0.948, 0.954, 0.9823 and 0.750, 0.713, 0.9988, respectively. Except for the specificity of cytoplasm segmentation, all metrics outperformed the best baseline models (P < 0.05) with a moderate margin.ConclusionsThe proposed algorithm achieved better performances on cervical cell segmentation than baseline models. It can be potentially used in automated cervical cancer cytology screening system.

Analysis and Segmentation of Cytoplasm with U-Net.

Analysis and Segmentation of Cytoplasm with U-Net.

Analysis and Segmentation of Cytoplasm with U-Net.

Analysis and Segmentation of Cytoplasm with U-Net.

The Role of WNT Pathway Mutations in Cancer Development and an Overview of Therapeutic Options.

It is well established that mutations in the canonical WNT-signalling pathway play a major role in various cancers. Critical to developing new therapeutic strategies is understanding which cancers are driven by WNT pathway activation and at what level these mutations occur within the pathway. Some cancers harbour mutations in genes whose protein products operate at the receptor level of the WNT pathway. For instance, tumours with RNF43 or RSPO mutations, still require exogenous WNT ligands to drive WNT signalling (ligand-dependent mutations). Conversely, mutations within the cytoplasmic segment of the Wnt pathway, such as in APC and CTNNB1, lead to constitutive WNT pathway activation even in the absence of WNT ligands (ligand-independent). Here, we review the predominant driving mutations found in cancer that lead to WNT pathway activation, as well as explore some of the therapeutic interventions currently available against tumours harbouring either ligand-dependent or ligand-independent mutations. Finally, we discuss a potentially new therapeutic avenue by targeting the translational apparatus downstream from WNT signalling.

Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography.

Clear cell renal cell carcinoma (ccRCC) is a common histopathological subtype of renal cancer and is notorious for its poor prognosis. Its accurate diagnosis by histopathology, which relies on manual microscopic inspection of stained slides, is challenging. Here, we present a correlative approach to utilize stained images and refractive index (RI) tomography and demonstrate quantitative assessments of the structural heterogeneities of ccRCC slides obtained from human patients. Machine-learning-assisted segmentation of nuclei and cytoplasm enabled the quantification at the subcellular level. Compared to benign regions, malignant regions exhibited a considerable increase in structural heterogeneities. The results demonstrate that RI tomography provides quantitative information in synergy with stained images on the structural heterogeneities in ccRCC.

Segmentation of Nucleus and Cytoplasm from H&amp;E-Stained Follicular Lymphoma

This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&amp;E-stained image is projected onto L*a*b* color space to quantify the visual differences. Secondly, this transformed image is segmented with the help of k-means clustering into its three cytological components (i.e., nuclei, cytoplasm, and extracellular), followed by pre-processing techniques in the third step, where adaptive thresholding and the area filling function are applied to give them proper shape for further analysis. Finally, the demarcation process is applied to pre-processed nuclei based on the local fitting criterion function for image intensity in the neighborhood of each point. Integration of these local neighborhood centers leads us to define the global criterion of image segmentation. Unlike active contour models, this technique is independent of initialization. This paper achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation.

HPV is a cargo for the COPI sorting complex during virus entry.

During entry, human papillomavirus (HPV) traffics from the cell surface to the endosome and then to the trans-Golgi network (TGN) and Golgi apparatus. HPV must transit across the TGN/Golgi and exit these compartments to reach the nucleus to cause infection, although how these steps are accomplished is unclear. Combining cellular fractionation, unbiased proteomics, and gene knockdown strategies, we identified the coat protein complex I (COPI), a highly conserved protein complex that facilitates retrograde trafficking of cellular cargos, as a host factor required for HPV infection. Upon TGN/Golgi arrival, the cytoplasmic segment of HPV L2 binds directly to COPI. COPI depletion causes the accumulation of HPV in the TGN/Golgi, resembling the fate of a COPI binding-defective L2 mutant. We propose that the L2-COPI interaction drives HPV trafficking through the TGN and Golgi stacks during virus entry. This shows that an incoming virus is a cargo of the COPI complex.

A Deep Learning approach for automated Cytoplasm and Nuclei cervical segmentation

Cervical cancer is a severe disease that affects many women in developing countries. Increasing screening capabilities is the best tool to reduce cancer incidence and save lives. Segmentation is an essential task in screening because it can lead to a better understanding of the morphological characteristics of cells. This paper presents a method for multi-class cell segmentation into Nuclei and Cytoplasm regions. Using the Herlev dataset for evaluation, this work achieved good performance by using state-of-the-art classification architectures such as EfficientNet combined with Feature Pyramid Networks to complete the segmentation task. Performance metrics for both classes show that the approach is robust enough to complete the task. The model achieved a 0.91 F1 score, 0.85 IoU, 0.91 Precision, 0.92 Recall, and 0.96 Specificity as class average with a very low standard deviation, validated with a 5-fold cross-validation. The proposal can help experts correctly asset cervical cell lesions and provide better healthcare for the patients.

Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells.

White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-intensive procedure. To address these issues, we present two novel shallow networks: a leukocyte deep segmentation network (LDS-Net) and leukocyte deep aggregation segmentation network (LDAS-Net) for the joint segmentation of cytoplasm and nuclei in WBC images. LDS-Net is a shallow architecture with three downsampling stages and seven convolution layers. LDAS-Net is an extended version of LDS-Net that utilizes a novel pool-less low-level information transfer bridge to transfer low-level information to the deep layers of the network. This information is aggregated with deep features in a dense feature concatenation block to achieve accurate cytoplasm and nuclei joint segmentation. We evaluated our developed architectures on four WBC publicly available datasets. For cytoplasmic segmentation in WBCs, the proposed method achieved the dice coefficients of 98.97%, 99.0%, 96.05%, and 98.79% on Datasets 1, 2, 3, and 4, respectively. For nuclei segmentation, the dice coefficients of 96.35% and 98.09% are achieved for Datasets 1 and 2, respectively. Proposed method outperforms state-of-the-art methods with superior computational efficiency and requires only 6.5 million trainable parameters.

Cervical Cell Segmentation Method Based on Global Dependency and Local Attention

The refined segmentation of nuclei and the cytoplasm is the most challenging task in the automation of cervical cell screening. The U-Shape network structure has demonstrated great superiority in the field of biomedical imaging. However, the classical U-Net network cannot effectively utilize mixed domain information and contextual information, and fails to achieve satisfactory results in this task. To address the above problems, a module based on global dependency and local attention (GDLA) for contextual information modeling and features refinement, is proposed in this study. It consists of three components computed in parallel, which are the global dependency module, the spatial attention module, and the channel attention module. The global dependency module models global contextual information to capture a priori knowledge of cervical cells, such as the positional dependence of the nuclei and cytoplasm, and the closure and uniqueness of the nuclei. The spatial attention module combines contextual information to extract cell boundary information and refine target boundaries. The channel and spatial attention modules are used to provide adaption of the input information, and make it easy to identify subtle but dominant differences of similar objects. Comparative and ablation experiments are conducted on the Herlev dataset, and the experimental results demonstrate the effectiveness of the proposed method, which surpasses the most popular existing channel attention, hybrid attention, and context networks in terms of the nuclei and cytoplasm segmentation metrics, achieving better segmentation performance than most previous advanced methods.

Computer-assisted tumor grading, validation of PD-L1 scoring, and quantification of CD8-positive immune cell density in urothelial carcinoma, a visual guide for pathologists using QuPath

BackgroundAdvances in digital imaging in pathology and the new capacity to scan high-quality images have change the way to practice and research in surgical pathology. QuPath is an open-source pathology software that offers a reproducible way to analyze quantified variables. We aimed to present the functionality of biomarker scoring using QuPath and provide a guide for the validation of pathologic grading using a series of cases of urothelial carcinomas.MethodsTissue microarrays of urothelial carcinomas were constructed and scanned. The images stained with HE, CD8 and PD-L1 immunohistochemistry were imported into QuPath and dearrayed. Training images were used to build a grade classifier and applied to all cases. Quantification of CD8 and PD-L1 was undertaken for each core using cytoplasmic and membrane color segmentation and output measurement and compared with pathologists semi-quantitative assessments.ResultsThere was a good correlation between tumor grade by the pathologist and by QuPath software (Kappa agreement 0.73). For low-grade carcinomas (by the report and pathologist), the concordance was not as high. Of the 32 low-grade tumors, 22 were correctly classified as low-grade, but 11 (34%) were diagnosed as high-grade, with the high-grade to the low-grade ratio in these misclassified cases ranging from 0.41 to 0.58. The median ratio for bona fide high-grade carcinomas was 0.59. Some of the reasons the authors list as potential mimickers for high-grade cases are fulguration artifact, nuclear hyperchromasia, folded tissues, and inconsistency in staining. The correlation analysis between the software and the pathologist showed that the CD8 marker showed a moderate (r = 0.595) and statistically significant (p < 0.001) correlation. The internal consistency of this parameter showed an index of 0.470. The correlation analysis between the software and the pathologist showed that the PDL1 marker showed a robust (r = 0.834) and significant (p < 0.001) correlation. The internal consistency of this parameter showed a CCI of 0.851.ConclusionsWe were able to demonstrate the utility of QuPath in identifying and scoring tumor cells and IHC quantification of two biomarkers. The protocol we present uses a free open-source platform to help researchers deal with imaging and data processing in the surgical pathology field.

Threshold estimation based on local minima for nucleus and cytoplasm segmentation

BackgroundImage segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to segment the regions of interest for further processing. We propose a method for segmenting the nuclei and cytoplasms from white blood cells (WBCs).MethodsInitially, the method computes an initial value based on the minimum and maximum values of the input image. Then, a histogram of the input image is computed and approximated to obtain function values. The method searches for the first local maximum and local minimum from the approximated function values in the order of increasing of knots sequence. We approximate the required threshold from the first local minimum and the computed initial value based on defined conditions. The threshold is applied to the input image to binarize it, and then post-processing is performed to obtain the final segmented nucleus. We segment the whole WBC before segmenting the cytoplasm depending on the complexity of the objects in the image. For WBCs that are well separated from red blood cells (RBCs), n thresholds are generated and then produce n thresholded images. Then, a standard Otsu method is used to binarize the average of the produced images. Morphological operations are applied on the binarized image, and then a single-pixel point from the segmented nucleus is used to segment the WBC. For images in which RBCs touch the WBCs, we segment the whole WBC using SLIC and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC.ResultsThe method is tested on two different public data sets and the results are compared to the state of art methods. The performance analysis shows that the proposed method segments the nucleus and cytoplasm well.ConclusionWe propose a method for nucleus and cytoplasm segmentation based on the local minima of the approximated function values from the image’s histogram. The method has demonstrated its utility in segmenting nuclei, WBCs, and cytoplasm, and the results are satisfactory.

An Improved Cervical Cell Segmentation Method Based on Deep Convolutional Network

The cervical cytology smear test is an effective method for cervical cancer early screening, and segmentation accuracy is essential for computer-aided diagnosis. In this study, an improved cervical nucleus and cytoplasm segmentation method based on a deep convolutional network was proposed. This method consisted of a cellular region proposal and pixel-level segmentation network (CRP-PSN). Data were obtained from the 2014 International Symposium on Biomedical Imaging cervical cell segmentation competition open dataset. In CRP networks, online hard example mining and soft-nonmaximum suppression algorithms were incorporated to solve problems, including background noise, impurities, and other interferences in smear images. For PSN networks, a generative adversarial network-generated adversarial network algorithm and least-squares loss function were used to generate cell region segmentation, thereby improving the cytoplasm segmentation results. Finally, the improved CRP-PSN model was analyzed and compared with other typical cervical cell segmentation methods. The experimental results showed that the proposed model could effectively improve the segmentation accuracy of cytoplasm and nuclei in cervical cytology smear images by 92% and 98.6%, respectively. These findings provided strong support for the application of this method for automated interpretation of cervical cytology smear images and improved diagnostic reliability.

A Deep Learning Approach for Automated Cytoplasm and Nuclei Cervical Segmentation

A Deep Learning Approach for Automated Cytoplasm and Nuclei Cervical Segmentation