Leukocyte Imaging Research Articles

White blood segmentation based on dual path and atrous spatial pyramid pooling

The count and recognition of white blood cells in blood smear images play an important role in the diagnosis of blood diseases including leukemia. Traditional manual test results are easily disturbed by many factors. It is necessary to develop an automatic leukocyte analysis system to provide doctors with auxiliary diagnosis, and blood leukocyte segmentation is the basis of automatic analysis. In this paper, we improved the U-Net model and proposed a segmentation algorithm of leukocyte image based on dual path and atrous spatial pyramid pooling. Firstly, the dual path network was introduced into the feature encoder to extract multi-scale leukocyte features, and the atrous spatial pyramid pooling was used to enhance the feature extraction ability of the network. Then the feature decoder composed of convolution and deconvolution was used to restore the segmented target to the original image size to realize the pixel level segmentation of blood leukocytes. Finally, qualitative and quantitative experiments were carried out on three leukocyte data sets to verify the effectiveness of the algorithm. The results showed that compared with other representative algorithms, the proposed blood leukocyte segmentation algorithm had better segmentation results, and the mIoU value could reach more than 0.97. It is hoped that the method could be conducive to the automatic auxiliary diagnosis of blood diseases in the future.

LeuFeatx: Deep learning–based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear

The abnormal growth of leukocytes causes hematologic malignancies such as leukemia. The clinical assessment methods for the diagnosis of the disease are labor-intensive and time-consuming. Image-based automated diagnostic systems can be of great help in the decision-making process for leukemia detection. A feature-dependent, intrinsic, reliable classifier is a critical component in building such a diagnostic system. However, the identification of vital and relevant features is a challenging task in the classification workflow. The proposed work presents a novel two-step methodology for the robust classification of leukocytes for leukemia diagnosis by building a VGG16-adapted fine-tuned feature-extractor model, termed as “LeuFeatx,” which plays a critical role in the accurate classification of leukocytes. LeuFeatx was found to be capable of extracting notable leukocyte features using microscopic single-cell leukocyte images. The filters and learned features are visualized and compared with base VGG16 model features. Independent classification experiments using three public benchmark leukocyte datasets were conducted to assess the effectiveness of extracted features with the proposed LeuFeatx model. Multiclass classifiers trained using LeuFeatx deep features achieved higher precision and sensitivity for seven leukocyte subtypes compared to the latest research on the AML Morphological dataset, and it achieved higher sensitivity for all cell types vis-à-vis recent work on peripheral blood cells dataset from the Hospital Clinic of Barcelona. In a binary classification experiment using the ALL_IDB2 dataset, classifiers trained using LeuFeatx deep features achieved an accuracy of 96.15%, which is better than the other state-of-the-art methods reported in the literature. Thus, the higher performance of the classifiers across observed comparison metrics establishes the relevance of the extracted features and the overall robustness of the proposed model.

Real-time imaging of inflammation and its resolution: It's apparent because it's transparent.

The ability to directly observe leukocyte behavior in vivo has dramatically expanded our understanding of the immune system. Zebrafish are particularly amenable to the high-resolution imaging of leukocytes during both homeostasis and inflammation. Due to its natural transparency, intravital imaging in zebrafish does not require any surgical manipulation. As a result, zebrafish are particularly well-suited for the long-term imaging required to observe the temporal and spatial events during the onset and resolution of inflammation. Here, we review major insights about neutrophil and macrophage function gained from real-time imaging of zebrafish. We discuss neutrophil reverse migration, the process whereby neutrophils leave sites of tissue damage and resolve local inflammation. Further, we discuss the current tools available for investigating immune function in zebrafish and how future studies that simultaneously image multiple leukocyte subsets can be used to further dissect mechanisms that regulate both the onset and resolution of inflammation.

Semi-Supervised Leukocyte Segmentation Based on Adversarial Learning With Reconstruction Enhancement

The number, relative ratio, and appearance of peripheral blood leukocytes can assist doctors to diagnose diseases such as lymphoma and leukemia. Therefore, segmentation of peripheral blood leukocytes in blood smear images plays a crucial role in the diagnosis of certain diseases. We propose a semi-supervised leukocyte segmentation method under the framework of adversarial learning, in which a discriminator is trained to differentiate the segmentation maps coming either from the ground truth or from the segmentation network. Specifically, we first construct a lightweight leukocyte segmentation network by modifying DeepLabV3+ with simplified MobileNetV2. In addition, a reconstruction decoder is developed as a constraint on feature extraction, to recover the original leukocyte image from its segmentation result. Then, we design the discriminator in a fully convolutional manner, which as well enables semi-supervised learning by inferring reliable supervisory information for unlabeled images. Quantitative and qualitative experimental results on four image datasets demonstrate the superiority of the proposed method over several state-of-the-art methods, achieving the highest average F1 score of 0.9717 and mIoU of 0.9493 with only 20% labeled training samples.

Computer‐aided deep learning model for identification of lymphoblast cell using microscopic leukocyte images

AbstractThe conventional technique of leukocyte cell classification involves segmenting the required portion of cells from input image, extracting features of the segmented nuclei, reducing and optimizing these features and then implements the classifier. Thus, designing a good classifier by using such techniques increases the time complexity of the system. In order to resolve such issues, the proposed work implements the deep convolutional neural network (DCNN)‐based models for classifying malignant versus normal WBCs. The proposed system is validated on 108 images of ALL‐IDB 1. Due to limited number of training samples, data augmentation is used to create a similar type of virtual image. In this work, experimentation is carried out for discrimination between normal and infected WBC using DCNN with four different activation functions. By using this method, a set of 6000 samples are generated and used for proper training of the DL model for all activation functions. The performance of each trained model is evaluated in terms of accuracy, recall, precision and F‐measure with the maximum values of 98.1%, 98.3%, 98.3% and 98.3% are achieved, respectively. Finally, it has been concluded that the defined DCNN model and ReLu activation function yield outstanding performance for lymphoblast characterization using microscopic blood images.

Automated segmentation of leukocyte from hematological images\u2014a study using various CNN schemes

Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

Labeled Leukocyte Imaging: Dawn of an Era.

More than one hundred years after the discovery of penicillin, infection remains a major threat to humankind. In 2017, more than 8 million deaths and 400,000 years of life lost were due to infection, making it first in morbidity and third in mortality among human diseases ([1][1]). Diagnosing

Leukocyte super-resolution via geometry prior and structural consistency.

.Significance: Researchers have made great progress in single-image super-resolution (SISR) using deep convolutional neural networks. However, in the field of leukocyte imaging, the performance of existing SISR methods is still limited as it fails to thoroughly explore the geometry and structural consistency of leukocytes. The inaccurate super-resolution (SR) results will hinder the pathological study of leukocytes, since the structure and cell lineage determine the types of leukocyte and will significantly affect the subsequent inspection.Aim: We propose a deep network that takes full use of the geometry prior and structural consistency of the leukocyte images. We establish and annotate a leukocyte dataset, which contains five main types of leukocytes (basophil, eosinophil, monocyte, lymphocyte, and neutrophil), for learning the structure and geometry information.Approach: Our model is composed of two modules: prior network and SR network. The prior network estimates the parsing map of the low-resolution (LR) image, and then the SR network takes both the estimated parsing map and LR image as input to predict the final high-resolution image.Result: Experiments show that the geometry prior and structural consistency in use obviously improves the SR performance of leukocyte images, enhancing the peak-signal-to-noise ratio (PSNR) by about 0.4 dB in our benchmark.Conclusion: As proved by our leukocyte SR benchmark, the proposed method significantly outperforms state-of-the-art SR methods. Our method not only improves the PSNR and structural similarity indices, but also accurately preserves the structural details of leukocytes. The proposed method is believed to have potential use in the wide-field cell prescreening by simply using a low-power objective.

Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm.

Acute myeloid leukemia (AML) is a fatal blood cancer that progresses rapidly and hinders the function of blood cells and the immune system. The current AML diagnostic method, a manual examination of the peripheral blood smear, is time consuming, labor intensive, and suffers from considerable inter-observer variation. Herein, a machine learning model to detect and classify immature leukocytes for efficient diagnosis of AML is presented. Images of leukocytes in AML patients and healthy controls were obtained from a publicly available dataset in The Cancer Imaging Archive. Image format conversion, multi-Otsu thresholding, and morphological operations were used for segmentation of the nucleus and cytoplasm. From each image, 16 features were extracted, two of which are new nucleus color features proposed in this study. A random forest algorithm was trained for the detection and classification of immature leukocytes. The model achieved 92.99% accuracy for detection and 93.45% accuracy for classification of immature leukocytes into four types. Precision values for each class were above 65%, which is an improvement on the current state of art. Based on Gini importance, the nucleus to cytoplasm area ratio was a discriminative feature for both detection and classification, while the two proposed features were shown to be significant for classification. The proposed model can be used as a support tool for the diagnosis of AML, and the features calculated to be most important serve as a baseline for future research.

Hybrid adversarial-discriminative network for leukocyte classification in leukemia.

Leukemia is a lethal disease that is harmful to bone marrow and overall blood health. The classification of white blood cell images is crucial for leukemia diagnosis. The purpose of this study is to classify white blood cells by extracting discriminative information from cell segmentation and combining it with the fine-grained features. We propose a hybrid adversarial residual network with support vector machine (SVM), which utilizes the extracted features to improve the classification accuracy for human peripheral white cells. Firstly, we segment the cell and nucleus by utilizing an adversarial residual network, which contains a segmentation network and a discriminator network. To extract features that can handle the inter-class consistency problem effectively, we introduce the adversarial residual network. Then, we utilize convolutional neural network (CNN) features and histogram of oriented gradient (HOG) features, which can extract discriminative features from images of segmented cell nuclei. To utilize the representative features fully, a discriminative network is introduced to deal with neighboring information at different scales. Finally, we combine the vectors of HOG features with those of CNN features and feed them into a linear SVM to classify white blood cells into six types. We used three methods to evaluate the effect of leukocyte classification based on 5000 leukocyte images acquired from a local hospital. The first approach is to use the CNN features as the input of SVM to classify leukocytes, which achieved 94.23% specificity, 95.10% sensitivity, and 94.41% accuracy. The use of the HOG features for SVM achieved 83.50% specificity, 87.50% sensitivity, and 85.00% accuracy. The use of combined CNN and HOG features achieved 94.57% specificity, 96.11% sensitivity, and 95.93% accuracy. We propose a novel hybrid adversarial-discriminative network for the classification of microscopic leukocyte images. It improves the accuracy of cell classification, reduces the difficulty and time pressure of doctors' work, and economizes the valuable time of doctors in daily clinical diagnosis.

Leukocyte Image Segmentation Based on Adaptive Histogram Thresholding and Contour Detection

Aims: The proposed method falls into the category of medical image processing. Background: Computer-aided automatic analysis systems for the analysis and cytometry of leukocyte (White Blood Cells, WBCs) in human blood smear images are a powerful diagnostic tool for many types of diseases, such as anemia, malaria, syphilis, heavy metal poisoning, and leukemia. Leukocyte segmentation is a basis of its automatic analysis, and the segmentation accuracy will directly influence the reliability of image-based automatic leukocyte analysis. Objective: This paper aims to present a leukocyte segmentation method, which improves segmentation accuracy under rapid and standard staining conditions. Methods: The proposed method first localizes leukocytes by color component combination and Adaptive Histogram Thresholding (AHT), and crops sub-image corresponding to each leukocyte. Then, the proposed method employs AHT to extract the nucleus of leukocyte and utilizes image color features to remove image backgrounds such as red blood cells and dyeing impurities. Finally, Canny edge detection is performed to extract the entire leukocyte. Accordingly, cytoplasm is obtained by subtracting nucleus with leukocyte. Results: Experimental results on two datasets containing 160 leukocyte images show that the proposed method obtains more accurate segmentation results than their counterparts. Conclusion: The proposed method obtains more accurate segmentation results than their counterparts under rapid and standard staining conditions.

A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network.

A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network is proposed. Firstly, a Siamese network with two identical convolutional neural network (CNN) sub-networks and a logistic regression for leukocyte five classification is designed, which can learn not only distinguishing features but also a similarity metric. Then for each category of the leukocytes, a typical sample is selected by the hematologist. To train the Siamese network, a leukocyte and a typical sample that belong to the same category make up a genuine pair and the leukocyte with the rest four typical samples respectively make up four impostor pairs. Obviously, the number of the genuine pairs is lesser than that of the impostor pairs. Thus, a data augmentation method suitable for leukocyte is used to enrich the amount of the genuine pairs. By training the Siamese network using the genuine pairs and impostor pairs, the Siamese network can not only shorten the similarity metric between the leukocyte and the same category of the typical sample but also increase the similarity metrics between the leukocyte and the different categories of the typical samples. Experimental results indicate that the proposed method can achieve 98.8% average testing accuracy. Graphical abstract.

Frontline Science: Dynamic cellular and subcellular features of migrating leukocytes revealed by in vivo lattice lightsheet microscopy.

Neutrophil and macrophage (Mϕ) migration underpin the inflammatory response. However, the fast velocity, multidirectional instantaneous movement, and plastic, ever-changing shape of phagocytes confound high-resolution intravital imaging. Lattice lightsheet microscopy (LLSM) captures highly dynamic cell morphology at exceptional spatiotemporal resolution. We demonstrate the first extensive application of LLSM to leukocytes in vivo, utilizing optically transparent zebrafish, leukocyte-specific reporter lines that highlighted subcellular structure, and a wounding assay for leukocyte migration. LLSM revealed details of migrating leukocyte morphology, and permitted intricate, volumetric interrogation of highly dynamic activities within their native physiological setting. Very thin, recurrent uropod extensions must now be considered a characteristic feature of migrating neutrophils. LLSM resolved trailing uropod extensions, demonstrating their surprising length, and permitting quantitative assessment of cytoskeletal contributions to their evanescent form. Imaging leukocytes in blood vessel microenvironments at LLSM's spatiotemporal resolution displayed blood-flow-induced neutrophil dynamics and demonstrated unexpected leukocyte-endothelial interactions such as leukocyte-induced endothelial deformation against the intravascular pressure. LLSM of phagocytosis and cell death provided subcellular insights and uncovered novel behaviors. Collectively, we provide high-resolution LLSM examples of leukocyte structures (filopodia lamellipodia, uropod extensions, vesicles), and activities (interstitial and intravascular migration, leukocyte rolling, phagocytosis, cell death, and cytoplasmic ballooning). Application of LLSM to intravital leukocyte imaging sets the stage for transformative studies into the cellular and subcellular complexities of phagocyte biology.

Multiphoton Intravital Microscopy of Mandibular Draining Lymph Nodes: A Mouse Model to Study Corneal Immune Responses.

Multiphoton intravital microscopy (MP-IVM) is a powerful tool to image cells in vivo. Its application in immunology research has opened new horizons, allowing intravital imaging of leukocytes at the single-cell level. A transparent cornea is vital to retain vision. As an immune privileged site, a rapid innate response to foreign antigens is crucial in clearing opportunistic bacterial and viral pathogens, and minimizing collateral structural damage to the cornea. Furthermore, dissecting the mechanisms and preventing the immunological rejection process after corneal transplantation is imperative to retain sight. Therefore, understanding the underlying mechanisms behind corneal immunity, specifically the process of antigen presentation and adaptive immunity in the mandibular draining lymph nodes (dLNs) in vivo, is crucial. Attempts of intravital imaging of mandibular dLNs have yielded little success to date, due to breathing artifacts and the location that is difficult to access. Herein, we present the first MP-IVM mouse model of the mandibular dLNs, utilizing transgenic mice in which CD11c+ cells are fluorescently labeled. Furthermore, we demonstrate that CD11c-YFP+ cells are localized mainly in the parafollicular cortex (T cell zone) and subcapsular area and are sparsely distributed in the follicular region (B cell zone) of mandibular dLNs during steady state. A significant increase in host CD11c-YFP+ cell density is noted at 14 and 21 days following allogeneic corneal transplantation, compared to steady state (p < 0.05). Moreover, allogeneic corneal transplantation results in increased host-derived CD11c-YFP+ cell mean speed and displacement in mandibular dLNs, compared to steady state (p < 0.001). The meandering index, an index for directionality, is significantly increased after allogeneic corneal transplantation at both 14 and 21 days, compared to steady state (p < 0.001). Taken together, our study demonstrates the necessary methodology required for intravital multiphoton imaging of the mandibular dLNs, allowing visualization of spatiotemporal kinetics of immune cells in vivo, and provides a window into the corneal immune reflex arc. This technique will be a powerful tool to investigate the pathogenesis of ocular immune and inflammatory diseases.

Impact of sample preparation on holographic imaging of leukocytes

The label-free analysis of leukocytes by holographic microscopy has the potential to overcome the extensive labeling procedures of conventional analysis by automated hematology analyzers and manual blood smear analysis. For the enrichment of leukocytes, selective lysis of erythrocytes is applied in flow cytometry to minimize the background. In the case of quantitative phase measurements, lysis procedures may affect the morphology of leukocytes inducing refractive index changes, which can deteriorate the specificity and sensitivity of the method. To study the effects of selective erythrocyte lysis on phase measurements of leukocytes, we compared two widespread methods: lysis by hypotonic shock followed by magnetic depletion of erythrocytes and lysis with a commercial lysis buffer containing ammonium chloride and potassium bicarbonate. We observed that lymphocytes were less affected than granulocytes and monocytes by both methods and that the intracellular changes increased with increasing incubation times. These findings indicate a significant effect of erythrocyte lysis on leukocytes and need to be studied for future hematology applications.

Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation.

Human peripheral blood leukocytes' classification is important for diagnosing blood diseases. Many microscopic leukocyte image automatic detection methods are proposed. In recent years, convolutional neural networks (CNNs) are applied to microscopic leukocyte image automatic classification. But when a CNN is used for microscopic leukocyte image classification, the dataset's scarcity and imbalance will lead to low classification accuracy. To improve classification accuracy, a data augmentation method is proposed, and a resampling method is adopted when using a CNN method. First, a deep CNN model for microscopic leukocyte image classification is designed. Then, a new data augmentation method based on feature concentration is proposed to enrich the dataset and overcome the problem of dataset scarcity. To make the CNN model focus on the leukocyte region, many images are generated by putting a segmented leukocyte into images with different microscopic surroundings using an image processing method. Finally, taking the imbalance of the five kinds of leukocytes in the dataset into consideration, a resampling method is adopted. The resampling method iteratively feeds the leukocyte images with a low proportion to the CNN model within an epoch to ensure that images of each of the five kinds of leukocytes are represented in relatively equal numbers in each batch. The experimental results demonstrate that the proposed classification method can achieve 97.6% average testing accuracy. Classification precision for the five kinds of leukocytes is above 93.4%, while sensitivity is above 92.5%. Both the proposed data augmentation and the resampling methods improve classification accuracy. A human peripheral blood leukocyte classification method based on a CNN and data augmentation is proposed. The problem of dataset scarcity is solved by the proposed data augmentation method, and the dataset imbalance is solved by a resampling method.

Estimation of White Blood Cells using Convolutional Neural Network

Normally blood samples contain red blood cells, white blood cells and platelets. White blood cells are also called as leukocytes and they are the cells of immune system. The measure of White Blood Cells is so important for the doctors in diagnosing various diseases like leukemia or tissue damage etc. So, counting of White Blood Cells plays an important role. The manual counting of White Blood Cells in medical laboratories involves a device called Haemocytometer. But this process is extremely monotonous, time consuming, and leads to inaccurate results. In this work, image processing and deep learning mechanisms are used to locate and classify the White Blood Cells based on their categories. The White Blood Cells which are classified are counted and compared with the standard range of the types available in the human blood sample. By comparing the availability of White Blood Cells types, the normal and the abnormal blood samples are predicted accordingly. The dataset of the normal blood sample is obtained from the laboratory in biotechnology department and the datasets used for training in Convolutional Neural Network are attained from the website Leukocyte Images for Segmentation and Classification (LISC). This will increase efficiency and reduce the doctor’s burden as traditional manual counting is dull, tedious, and possibly subjective.

Development of Blood-Cell-Selective Fluorescent Biodots for Lysis-Free Leukocyte Imaging and Differential Counting in Whole Blood.

Complete blood count with leukocyte (white blood cell, WBC) differential is one of the most frequently ordered clinical test for disease diagnosis. Herein, multifunctional fluorescent carbon dots derived from biomolecules (biodots) for rapid lysis-free whole blood analysis are developed. Specifically, two types of biodots are molecularly engineered through hydrothermal synthesis for differential blood cells labeling. Type I biodots synthesized from amino acid (serine/threonine) precursors and passivated with polyethylenimine can label both red blood cells (RBCs) and WBCs with excellent contrast in fluorescence intensity, enabling direct counting of leukocytes in whole blood samples without a tedious RBC lysis step. It also allows three-part leukocyte differential counting by flow cytometry without using expensive fluorophore-conjugated antibodies. On the other hand, Type II biodots synthesized from the same amino acid precursors but passivated with a biopolymer (chitosan) are able to selectively lyse RBCs with greater than 98% efficiency to allow simultaneous fluorescent labeling of leukocytes for WBC counting in whole blood. It is envisioned that these novel nanoreagents, which eliminate the cumbersome lysis and antibody conjugation steps for selective labeling of different blood cells, would revolutionize disease diagnostics toward achieving faster, cheaper, and more accurate whole blood analyses in one test.

Is 99mTc-HMPAO-leukocyte imaging an accurate method in evaluating therapy result in prosthetic joint infection and diagnosing suspected chronic prosthetic joint infection?

To investigate the sensitivity and specificity of 99mTc-HMPAO-leukocyte imaging in evaluating therapy result in patients with prosthetic joint infection (PJI) and in diagnosing suspected chronic PJI. Sixty-two patients (63 joints) with microbiologically verified PJI were examined by leukocyte imaging to evaluate therapy result during or at the end of antibiotic treatment or if the patient had a chronic PJI after treatment. Group 1 consisted of 49 patients with on-going or within less than 14 days of ending antibiotic treatment examined to evaluate response. Group 2 consisted of 13 patients examined after completed treatment on suspicion of chronic PJI with no or recently initiated renewed antibiotic treatment. This study applied a combination of different imaging approaches of 99mTc-HMPAO-leukocyte scintigraphy: delayed and late planar images, bone marrow imaging and SPECT/CT imaging. All joints were examined with at least two of the approaches and 53 joints with all three approaches. The report was based on the combined results of the approaches used. A chronic PJI was confirmed with a positive microbiological culture. A cured infection was confirmed with either a negative culture or at least 24 months antibiotic-free follow-up with no relapse. In the therapy evaluation group sensitivity was 0.57 and specificity was 0.81. In the suspected chronic infection group sensitivity was 1.00 and specificity 0.91. 99mTc-HMPAO-leukocyte imaging appears to be an accurate method to diagnose or exclude chronic PJI, but cannot be recommended for therapy evaluation of PJI in patients with on-going antibiotic treatment.

Two-Photon Intravital Imaging of Leukocytes in the Trachea During Pneumococcal Infection.

Two-photon intravital imaging (2P-IVM) of the murine trachea is a powerful technique for real-time imaging of immune cell recruitment and trafficking during airborne pathogen infections. Neutrophils are an important component of the innate immune response that are able to rapidly infiltrate the airway mucosa in response to Streptococcus pneumoniae infection. Here we describe a protocol to visualize in vivo neutrophil extravasation and cell dynamics in the tracheal tissue of a S. pneumoniae-infected mouse using 2P-IVM. To perform this protocol, we infected and imaged the trachea of a lysozyme M green fluorescent protein (LysM-GFP) mouse, in which neutrophils express GFP. Additionally, we used a custom-designed platform, which allowed the intubation and fixation of the trachea after surgical exposition, and we injected intravenously a fluorescently labeled dextran solution to visualize the blood vessels.