White Blood Cell Segmentation Research Articles

Nucleus segmentation of white blood cells in blood smear images by modeling the pixels' intensities as a set of three Gaussian distributions.

The precise segmentation of white blood cells (WBCs) within blood smear images is a significant challenge with implications for both medical research and image processing. Of particular importance is the often neglected task of accurately segmenting WBC nuclei, an aspect that currently lacks dedicated methodologies. This paper introduces a straightforward and efficient method designed to fill this critical gap, providing an effective solution for the efficient segmentation of WBC nuclei. In blood smear imagery, the distinctive coloration of WBCs contrasts with the hues of other blood components. The inherent obscurity of WBCs prompts their segmentation by isolating pixels with minimal intensities. To streamline this process, our proposed method employs the Laplacian pyramid technique to decorrelate pixels in blood smear images, thereby amplifying the contrast. Subsequently, the intensities of pixels constituting blood cells, encompassing WBCs and the background, are modeled using three Gaussian random variables. Capitalizing on this feature, we implement the Gaussian mixture model (GMM) clustering method to determine the optimal threshold value, facilitating a highly precise segmentation of WBC nuclei. The proposed method demonstrates the capability to process images containing a single WBC as well as effectively functioning with images containing multiple cells of this type. Evaluation of the method on the ALL-IDB, ALL-IDB2, CellaVision, and JTSC datasets yielded accuracy values of 0.9802, 0.9725, 0.9772, and 0.9730, respectively. Comparative analysis with state-of-the-art methods revealed a notably comparable performance, underscoring the effectiveness of the proposed approach. The method presented in this article is highly competitive for segmenting the nuclei of WBCs compared to state-of-the-art methods. The three main advantages of our method are its ability to process images containing one or more WBCs, the automatic calculation of threshold values for each processed image, eliminating the need for manual parameter adjustments. Lastly, the method is efficient, as its algorithmic complexity is approximately .

Blood cell image segmentation and classification: a systematic review.

Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

An indeterminacy fusion of encoder-decoder network based on neutrosophic set for white blood cells segmentation

Medical image analysis, particularly the segmentation of White Blood Cells (WBCs), holds critical importance in scrutinizing the quantity and morphology of these cells in smear images, a pivotal step in disease detection. Existing methodologies often fall short in differentiating between healthy and diseased WBCs, emphasizing color components over internal organizational nuances and external morphologies. This paper presents a pioneering contribution through the introduction of an encoder-decoder deep neural network with an indeterminacy fusion-based model explicitly designed for WBC extraction from blood images. In contrast to prior studies, our approach addresses the inadequacies by delving into the intricacies of WBC structure, characterizing object indeterminacy within the neutrosophic set domain. The encoder-decoder network is fortified by integrating WBC indeterminacy as a fusion component, enabling the segmentation of WBCs into distinct nucleus and cytoplasm regions. The efficacy of our methodology is evaluated across three datasets with varying resolutions. Quantitative metrics such as segmentation accuracy and intersection over union (IoU) are employed to assess the proposed network's performance, which consistently outperforms three original encoder-decoder networks. Notably, our model achieves commendable precision rates, with the highest mean segmentation accuracy reaching 0.95301 across the three datasets. These results underscore the enhanced performance of our segmentation network, attributing its success to the influential role of indeterminacy fusion in augmenting conventional segmentation methods. In summary, this work not only introduces a novel solution for WBC segmentation but establishes its superiority over existing methods. The interpretive insights gained from the results highlight the potential of indeterminacy fusion to drive advancements in the realm of medical image segmentation, emphasizing originality, significance, and superior performance metrics.

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation.

Accurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion. The proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance. Experimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost. Our method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.

DEEP LEARNING FOR SEGMENTATION AND COUNTING OF WHITE BLOOD CELLS IN CLINICAL DIAGNOSIS

In the field of cytopathology, the accurate identification and counting of white blood cells (WBCs) in blood smears is crucial for diagnosing various types of cancer. The process of manually detecting and segmenting these structures, however, can be challenging due to their variable morphologies and the presence of overlapping objects in the images. This makes manual detection time-consuming, labor-intensive, and prone to error, particularly for individuals without extensive experience in cytopathology. In this paper, a deep learning algorithm is developed based on a Mask R-CNN model and driven by a sub-algorithm called KOWN (Keep Only White Blood Cells with Nuclei) for WBC segmentation and counting. The proposed algorithm improves the accuracy of measurements compared to other rapidly growing deep learning works, providing maximum precision in detecting and counting WBCs in both low- and high-blood-cell-density images.

Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation

Leukemia is a significant health challenge, with high incidence and mortality rates. Computer-aided diagnosis (CAD) has emerged as a promising approach. However, deep-learning methods suffer from the “black box problem”, leading to unreliable diagnoses. This research proposes an Explainable AI (XAI) Leukemia classification method that addresses this issue by incorporating a robust White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism. The segmentation of WBC is achieved by combining image processing and U-Net techniques, resulting in improved overall performance. The segmented images are fed into modified ResNet-50 models, where the MLP classifier, activation functions, and training scheme have been tested for leukemia subtype classification. Additionally, we add visual explainability and feature space analysis techniques to offer an interpretable classification. Our segmentation algorithm achieves an Intersection over Union (IoU) of 0.91, in six databases. Furthermore, the deep-learning classifier achieves an accuracy of 99.9% on testing. The Grad CAM methods and clustering space analysis confirm improved network focus when classifying segmented images compared to non-segmented images. Overall, the proposed visual explainable CAD system has the potential to assist physicians in diagnosing leukemia and improving patient outcomes.

Deep learning can predict presence of TP53 aberrations and IGHV mutational status from peripheral blood smears of chronic lymphocytic leukemia

Introduction: Patient stratification based on genomic biomarkers is critical to optimize treatment of chronic lymphocytic leukemia (CLL). Using digitized, genomically annotated peripheral blood smears (PBS) of patients with CLL, we evaluated whether deep learning can predict presence of TP53 aberrations (del[17p] and/or TP53 mutation) and unmutated IGHV status solely based on cytomorphology. Methods: The workflow consisted of sample collection and digitization, manual annotation, segmentation, morphological feature extraction, and aberration classification. Whole slide images were generated from PBS of 414 patients with previously untreated CLL (CLL14, GIVe, BAAG). Manual annotation was conducted using Qupath software (v0.3.2) with ImageJ. A tailored Stardist model was used for semantic segmentation. Morphological features were collected from each segmented white blood cell (WBC) and fed into the classification model. Two neural network (NN) models were generated for each aberration (TP53 and IGHV) based on a cohort split into 70/30% training and testing datasets. The models were evaluated using AUC (Area Under the Curve of ROC [Receiver Operating Curve]). Results: In preparation of model training, 4,962 manual cells from 18 randomly selected patients were manually annotated. For the TP53 classifier, 141,873 WBC from 52 patients were segmented using the Stardist model. For the IGHV classifier, 131,410 WBC from 57 patients were segmented. The model was trained on 256 × 256 pixels patches obtained from original images with 200 epochs and 100 batch_size whilst making use of data augmentation. An intersection over union (IoU) value of 89.6% confirmed good model performance, i.e. accurate WBC segmentation. Lymphocyte cells were then segmented on each frame image. In total, 28 single-cell morphological features were captured and 8 best representatives fed into the NN model. The collected metadata vector features were randomly assigned to non-overlapping training and testing sets. Separate datasets were used for the TP53 and IGHV classifier. On a single-cell level, the models achieved an AUC of 86% for TP53 aberrations and IGHV status, respectively. For independent validation, the TP53 and IGHV classifiers were tested on a set of unseen images of 33 and 22 patients, respectively. AUC values of 78% and 72%, respectively, were achieved, confirming good model performance. To classify individual patients, a threshold of ≥50% positive cells/image was used to either classify a patient as TP53 aberrated or IGHV unmutated. Using this approach, 70% of patients with TP53 aberration and 68% with IGHV unmutated status were accurately classified. Keywords: Bioinformatics, Chronic Lymphocytic Leukemia (CLL), Computational and Systems Biology, Diagnostic and Prognostic Biomarkers No conflicts of interests pertinent to the abstract.

CUSS-Net: A Cascaded Unsupervised-Based Strategy and Supervised Network for Biomedical Image Diagnosis and Segmentation.

Biomedical image segmentation and classification are critical components in a computer-aided diagnosis system. However, various deep convolutional neural networks are trained by a single task, ignoring the potential contribution of mutually performing multiple tasks. In this paper, we propose a cascaded unsupervised-based strategy to boost the supervised CNN framework for automated white blood cell (WBC) and skin lesion segmentation and classification, called CUSS-Net. Our proposed CUSS-Net consists of an unsupervised-based strategy (US) module, an enhanced segmentation network named E-SegNet, and a mask-guided classification network called MG-ClsNet. On the one hand, the proposed US module produces coarse masks that provide a prior localization map for the proposed E-SegNet to enhance it in locating and segmenting a target object accurately. On the other hand, the enhanced coarse masks predicted by the proposed E-SegNet are then fed into the proposed MG-ClsNet for accurate classification. Moreover, a novel cascaded dense inception module is presented to capture more high-level information. Meanwhile, we adopt a hybrid loss by combining a dice loss and a cross-entropy loss to alleviate the imbalance training problem. We evaluate our proposed CUSS-Net on three public medical image datasets. Experiments show that our proposed CUSS-Net outperforms representative state-of-the-art approaches.

Chaotic fitness-dependent quasi-reflected Aquila optimizer for superpixel based white blood cell segmentation

Chaotic fitness-dependent quasi-reflected Aquila optimizer for superpixel based white blood cell segmentation

Abstract 136: Canonical Correlation Analysis Of Histomic And Transcriptomic Data From Ischemic Stroke Thrombi Identifies Complex Traits Associated With Etiology

Background: Determination of ischemic stroke etiology is essential for guiding post-operative stroke treatment and secondary prevention. Hypothesis: Canonical correlation analysis (CCA) of paired clot histomic and transcriptomic features can identify etiology-specific white blood cell (WBC) image-omic correlates. Methods: We analyzed paired H&E histology and transcriptome data from 31 clots (large artery atherosclerosis, LAA=5; cardio embolism, CE=18; stroke of other cause, OTH=3; cryptogenic, CR=5). A published computational pipeline for WBC segmentation and characterization was used to quantify 46 image features (e.g., textures representative of heterochromatin-euchromatin ratio) from >100,000 WBC instances. Features were used to classify WBCs into 1 of 10 validated classes and calculate clot class frequency distributions. SparseCCA was used to test associations among image features and expression signatures. Gene ontology (GO) enrichment analysis was used to identify biological process (BP) ontologies that were associated with positively correlated genes for each etiology. Results: Canonical covariates (e.g., histology canonical variable [CV] 1 and transcriptome CV1) were strongly and significantly correlated ( R >0.9, p <0.001). Pairwise correlation of CVs and original feature sets (e.g., to identify WBC features captured in histology CVs) showed the most enriched CVs in LAA (CV6, top row of Figure), CE (CV14, middle row of Figure), and OTH (CV17, bottom row of Figure). These represented distinct WBC subtypes on histology and unique GO signatures. LAA clots had enrichment of leukocytes (and greater T-cell regulation and cytokine production), while CE clots had granulocyte enrichment, and OTH clots had many WBCs embedded in RBC-rich regions with greater heme biosynthesis. Conclusion: We found significant structure-expression covariates representing biologically-interpretable traits that are different among stroke etiologies.

White blood cell segmentation by using vector color approach

White blood cells play an important role in protecting the body from various types of diseases, therefore blood tests are done to determine a person’s disease. Diagnosis of blood diseases is done by analyzing and counting the white blood cells in the blood. Optimal calculations and analysis of white blood cell are important, so research on white blood cell detection is a rapidly growing topic. Many detection methods are proposed using the existing segmentation techniques. In this paper, the detection method is proposed using vector operation approach instead general segmentation technique. From the experiments performed, it is shown that the proposed method can detect white blood cells on smear images. Further, this method extracts the nucleus from white blood cells well.

Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network.

Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Significant research has been undertaken in the area of AL diagnosis automation in order to deliver an accurate diagnosis. This study addresses the morphological classification of atypical white blood cells (WBCs), including immature WBCs and atypical lymphocytes, in acute myeloid leukemia (AML), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features. Using a hybrid model based on geometric transformation (GT) and a deep convolutional autoencoder (DCAE), this work provides a novel technique in the field of AI for resolving the issue of imbalanced distribution of WBCs in blood samples, nicknamed the "GT-DCAE WBC augmentation model". In addition, to extract context-free atypical WBC features, this study develops a stable learning paradigm by incorporating WBC segmentation into deep learning. In order to classify atypical WBCs into eight distinct subgroups, a hybrid multiclassification model termed the "two-stage DCAE-CNN atypical WBC classification model" (DCAE-CNN) was developed. The model achieved an average accuracy of 97%, a sensitivity of 97%, and a precision of 98%. Overall and by class, the model's discriminating abilities were exceptional, with an AUC of 99.7% and a class-wise range of 80% to 100%.

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach. Through entropy-adaptive mask generation, WBCs accurately detect the circularity region for identification of the nucleus. The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations. Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions. The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images. The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier. The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes, monocytes, neutrophils, eosinophils, and abnormalities in the WBCs. The proposed HTsC identifies the abnormal activity in the WBC, considering the color and shape features. It exhibits a higher classification accuracy value of 99.6% when combined with the other classifiers. The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%, which is approximately 3%–12% higher than the conventional technique.

Segmentation and Classification of White Blood Cells Using the UNet.

In the bone marrow, plasma cells are made up of B lymphocytes and are a type of WBC. These plasma cells produce antibodies that help to keep bacteria and viruses at bay, thus preventing inflammation. This presents a major challenge for segmenting blood cells, since numerous image processing methods are used before segmentation to enhance image quality. White blood cells can be analyzed by a pathologist with the aid of computer software to identify blood diseases accurately and early. This study proposes a novel model that uses the ResNet and UNet networks to extract features and then segments leukocytes from blood samples. Based on the experimental results, this model appears to perform well, which suggests it is an appropriate tool for the analysis of hematology data. By evaluating the model using three datasets consisting of three different types of WBC, a cross-validation technique was applied to assess it based on the publicly available dataset. The overall segmentation accuracy of the proposed model was around 96%, which proved that the model was better than previous approaches, such as DeepLabV3+ and ResNet-50.

Automated segmentation of Acute Lymphocytic Leukemia (ALL) subtypes by the combination of color space conversion and K-means cluster

Leukemia is blood cancer, and it is one of the most common and deadly causes of death in the world. Morphologically, Leukemia cells are classified into three types of L1, L2, and L3 by the French‑American‑British (FAB) classification. A new method of automatically segmenting blast cells from microscopic blood smear images proposed in this research. This study proposes significant pre-processing to obtain high segmentation performance and presents a new combination of image processing approaches. specially, the five color spaces selected with K-means cluster to segment subtypes of Acute Lymphocytic Leukemia. The majority of the components of the performance color space were chosen based on their similarity with ground truth image through using five evaluation parameters. The proposed codes for Acute Lymphocytic Leukemia subtype accurate segmentation applied on local and public datasets (427 images). 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WBC image classification and generative models based on convolutional neural network

BackgroundComputer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system.Methods(i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing.Results(i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work.ConclusionThis work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.

Modified U‐Net for cytological medical image segmentation

AbstractDeep learning–based medical image segmentation is henceforth widely established as a powerful segmentation process. This article proposes a new U‐Net architecture based on a convolutional neural network for cytology image segmentation. This structure is more suitable to take into account pixel neighborhood in deconvolution. The goal is to develop an accurate segmentation method for white blood cells segmentation based on cells types features. This new proposed method yields a significant improvement compared to our previous work on the cytological medical dataset. In addition, the performance of the new architecture was also successfully tested on the Digital Retinal Image for Vessel Extraction databases benchmark. The images of this challenge are similar to our cytology image segmentation. Our approach achieved 25% relative improvement of the accuracy compared to the state‐of‐the‐art.

Modification of IDTCS Method for Touching Leukemia Cell Grouping

Morphological analysis and calculation of the number of white blood cells on microscopic images are stages in diagnosing leukemia. Constraints in developing a system for diagnosing leukemia are white blood cell segmentation and counting of the number single cells in touching cell. We propose to modify the Iterative Distance Transform For Convex Sets (IDTCS) method to separate the touching leukemia cells. The IDTCS method is used to determine markers for each cell in touching cells. The marker results from the IDTCS method are used as cell centroids and the next process is pixels clustering based on the nearest cell centroid using the euclidean distance function. The data used are microscopic images of Acute Lymphoblastic Leukemia (ALL). The experimental results show that using modified IDTCS method for clustering produces better accuracy compared to the K-Means clustering and Watershed methods.

Modification of IDTCS Method for Touching Leukemia Cell Grouping

Morphological analysis and calculation of the number of white blood cells on microscopic images are stages in diagnosing leukemia. Constraints in developing a system for diagnosing leukemia are white blood cell segmentation and counting of the number single cells in touching cell. We propose to modify the Iterative Distance Transform For Convex Sets (IDTCS) method to separate the touching leukemia cells. The IDTCS method is used to determine markers for each cell in touching cells. The marker results from the IDTCS method are used as cell centroids and the next process is pixels clustering based on the nearest cell centroid using the euclidean distance function. The data used are microscopic images of Acute Lymphoblastic Leukemia (ALL). The experimental results show that using modified IDTCS method for clustering produces better accuracy compared to the K-Means clustering and Watershed methods.

MSS-WISN: Multiscale Multistaining WBCs Instance Segmentation Network

Accurate segmentation and detection (instance segmentation) of white blood cells (WBCs) from whole slide images remains a challenging task, as the WBCs vary widely in shapes, sizes, and colors caused by different cell subtypes and various staining techniques. In this paper, we propose a novel framework for end-to-end segmentation and detection of WBCs that are on multiple scales and stained by different techniques. We name the framework the multi-scale and multi-staining WBC instance segmentation network (MSS-WISN). The MSS-WISN consists of two parts: 1) a feature extraction network for strengthening the feature expression and minimizing the impact of different staining techniques, and 2) a feature fusion network for highlighting salient features and thereby eliminating the effect of scale variations. To verify the effectiveness of the MSS-WISN, we build a new dataset containing 302 Magenta stained images (collected by Tianjin Medical University) and 242 Wright stained images (from a public dataset). Experiments show that the proposed framework outperforms other state-of-the-art methods in terms of WBC detection and WBC segmentation.