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Tong B, Wen T, Du Y, Pan T. Cell image instance segmentation based on PolarMask using weak labels. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107426. [PMID: 36827825 DOI: 10.1016/j.cmpb.2023.107426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE A PolarMask-based method for blood cell contour segmentation is proposed. The method is divided into two parts. One part is a weak label-based model pretraining method, which uses weak labels to train the model and obtain a pretraining weight. The training speed and accuracy of the segmentation model are accelerated. The other part is based on the PolarMask method to segment the white and red blood cells in blood cells and can obtain smoother cell contours. Thus, it improves the accuracy of blood cell segmentation. Our method can help medical personnel identify the number of cells and cell shape quickly, which reduces the workload for medical personnel. METHODS We improve PolarMask to make it more suitable for blood cell contour segmentation, and the improved method can be divided into two parts. In the first part, we use a weakly labeled dataset with the labeling type of bounding boxes for pretraining and then use the labels of the segmentation type for transfer learning of the cell segmentation model. In the second part, we add a smoothing constraint loss to the loss function of the mask to smoothen the segmented cell contours. We add the SE attention mechanism in the backbone network (ResNet18) to further improve the segmentation accuracy. RESULTS Our method is mainly used for the segmentation of blood cell (erythrocyte and leukocyte) contours. Our method improves average precision (AP) by 8.4% and AP50 by 0.6% compared with PolarMask. The most significant improvement is in AP75, which improves by 8.8%. CONCLUSION Our method models blood cell contours based on PolarMask and uses a weakly labeled training model to obtain pretrained weights that can segment red and white blood cells. Our method effectively improves the accuracy of the model in segmenting blood cells, and the segmented blood cell contours are smoother.
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Affiliation(s)
- Binbin Tong
- College of Engineering, Huaqiao University, Quanzhou, 362021, China
| | - Tingxi Wen
- College of Engineering, Huaqiao University, Quanzhou, 362021, China.
| | - Yu Du
- College of Engineering, Huaqiao University, Quanzhou, 362021, China
| | - Ting Pan
- College of Engineering, Huaqiao University, Quanzhou, 362021, China
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PF-ViT: Parallel and Fast Vision Transformer for Offline Handwritten Chinese Character Recognition. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8255763. [PMID: 36211021 PMCID: PMC9534625 DOI: 10.1155/2022/8255763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/05/2022] [Accepted: 08/16/2022] [Indexed: 11/20/2022]
Abstract
Recently, Vision Transformer (ViT) has been widely used in the field of image recognition. Unfortunately, the ViT model repeatedly stacks 12-layer encoders, resulting in a large number of model computations, many parameters, and slow training speed, making it difficult to deploy on mobile devices. In order to reduce the computational complexity of the model and improve the training speed, a parallel and fast Vision Transformer method for offline handwritten Chinese character recognition is proposed. The method adds parallel branches of the encoder module to the structure of the Vision Transformer model. Parallel modes include two-way parallel, four-way parallel, and seven-way parallel. The original picture is fed to the encoder module after flattening and linear embedding processing operations. The core step in the encoder is the multihead attention mechanism. Multihead self-attention can learn the interdependence between image sequence blocks. In addition, the use of data expansion strategies increases the diversity of data. In the two-way parallel experiment, when the model is 98.1% accurate on the dataset, the number of parameters and the number of FLOPs are 43.11 million and 4.32 G, respectively. Compared with the ViT model, whose parameters and FLOPs are 86 million and 16.8 G, respectively, the two-way parallel model has a 50.1% decrease in parameters and a 34.6% decrease in FLOPs. This method has been demonstrated to effectively reduce the computational complexity of the model while indirectly improving image recognition speed.
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Jung C, Abuhamad M, Mohaisen D, Han K, Nyang D. WBC image classification and generative models based on convolutional neural network. BMC Med Imaging 2022; 22:94. [PMID: 35596153 PMCID: PMC9121596 DOI: 10.1186/s12880-022-00818-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 05/06/2022] [Indexed: 11/25/2022] Open
Abstract
Background Computer-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. Conclusion This 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.
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Affiliation(s)
- Changhun Jung
- Department of Cyber Security, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea
| | - Mohammed Abuhamad
- Department of Computer Science, Loyola University Chicago, 1032 W Sheridan Rd, Chicago, 60660, USA
| | - David Mohaisen
- Department of Computer Science, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL, 32816, USA
| | - Kyungja Han
- Department of Laboratory Medicine and College of Medicine, The Catholic University of Korea Seoul St. Mary's Hospital, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - DaeHun Nyang
- Department of Cyber Security, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Republic of Korea.
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M.Roy R, P.M. A. Segmentation of leukocyte by semantic segmentation model: A deep learning approach. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102385] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Lu Y, Qin X, Fan H, Lai T, Li Z. WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.107006] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Mondal C, Hasan MK, Ahmad M, Awal MA, Jawad MT, Dutta A, Islam MR, Moni MA. Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100794] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Wang C, Zhang H, Li Z, Zhou X, Cheng Y, Chen R. White Blood Cell Image Segmentation Based on Color Component Combination and Contour Fitting. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017102310] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
White Blood Cell (WBC) image segmentation plays a key role in cell
morphology analysis. However, WBC segmentation is still a challenging task due to the diversity
of WBCs under different staining conditions.
Objective:
In this paper, we propose a novel WBC segmentation method based on color component
combination and contour fitting to segment WBC images accurately.
Methods:
Specifically, the proposed method first uses color component combination and image
thresholding to achieve nucleus segmentation, then uses a color prior to remove image background,
and extracts the initial WBC contour via Canny edge detection, and finally judges and
closes the unclosed WBC contour by contour fitting. Accordingly, cytoplasm segmentation is
achieved by subtracting the nucleus region from the WBC region.
Results:
Experimental results on 100 WBC images under rapid staining condition and 50 WBC
images under standard staining condition showed that the proposed method improved segmentation
accuracy of white blood cells under rapid and standard staining conditions.
Conclusion:
The proposed color component combination and contour fitting is effective in WBC
segmentation task.
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Affiliation(s)
- Chuansheng Wang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Hong Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
| | - Xiaogen Zhou
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
| | - Yong Cheng
- School of Information Mechanical & Electrical Engineering, Jiangsu Open University, Nanjing, China
| | - Rongyan Chen
- Department of Clinical Laboratory, the People's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, China
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FAB classification of acute leukemia using an ensemble of neural networks. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00491-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Al-Dulaimi K, Banks J, Nugyen K, Al-Sabaawi A, Tomeo-Reyes I, Chandran V. Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review-Challenges, Current and Future Potential Techniques. IEEE Rev Biomed Eng 2020; 14:290-306. [PMID: 32746365 DOI: 10.1109/rbme.2020.3004639] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images. Performance of existing methods have been comprehensively compared, taking into account databases used, number of images and limitations. This study can also help us to identify the challenges that remain, in achieving a robust analysis of white blood cell microscope images, which could support the diagnosis of blood disorders and assist researchers and pathologists in the future. The impact of this work is to enhance the accuracy of pathologists' decisions and their efficiency, and overall benefit the patients for faster and more accurate diagnosis. The significant of the paper on intelligent system is that provides future potential techniques for solving overlapping white blood cell identification and other problems microscopic images. The accurate segmentation and detection of white blood cells can increase the accuracy of cell counting system for diagnosing diseases in the future.
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Jha KK, Dutta HS. Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images. Med Biol Eng Comput 2019; 58:171-186. [PMID: 31811554 DOI: 10.1007/s11517-019-02071-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 11/05/2019] [Indexed: 01/19/2023]
Abstract
Acute lymphoblastic leukaemia (ALL), which is due to the malfunctioning in the bone marrow, is common among people all over the world. The haematologist suffers a lot to discriminate the presence of leukaemia in the patients using the blood smears. To overcome the inaccuracy and reliability issues, this paper proposes an automatic method of leukaemia detection, named chronological Sine Cosine Algorithm-based actor-critic neural network (Chrono-SCA-ACNN). Initially, the blood smear images are segmented using the proposed entropy-based hybrid model, from which the image-level features and statistical features are extracted from the segments. Then, the selected features are applied to the proposed classifier, which detects the leukaemia. In the proposed Chrono-SCA-ACNN, the optimal weights are selected by the proposed Chrono-SCA, which is the integration of the chronological concept in the SCA. Finally, the experimentation is performed using the ALL-IDB2 database, and the effectiveness of the proposed method over the existing methods is evaluated. From the analysis, the accuracy of the proposed method is found to be 0.99, which proves that it outperforms the existing classification methodologies. Graphical abstract Block diagram of proposed Leukaemia detection. The main aim of the paper is to segment and classify the WBCs for ALL detection in single cell blood smear images. Initially, the blood smear image is subjected to pre-processing in order to enhance the quality of the input image so as to make it effective for the further processes associated with Leukaemia detection. The pre-processed image is applied to the segmentation process that segments the cytoplasm and nucleus using the Entropy-based hybrid model. The entropy-based hybrid model is developed using the FCM and active contour to segment the cytoplasm and nucleus that is fused using the entropy. The segments are subjected to the feature extraction that extracts the statistical features and the color histogram-based features from the segments. The features are presented to the Actor-Critic Neural Network and the weights of the Neural Network (NN) are optimally tuned using the proposed Chrono-SCA. The block diagram of the proposed method of leukaemia detection is depicted in Fig. 1.
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Affiliation(s)
- Krishna Kumar Jha
- Calcutta Institute of Technology, Banitabla, Uluberia, Howrah - 711316, India.
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11
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Al-jaboriy SS, Sjarif NNA, Chuprat S, Abduallah WM. Acute lymphoblastic leukemia segmentation using local pixel information. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.03.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Andrade AR, Vogado LHS, Veras RDMS, Silva RRV, Araujo FHD, Medeiros FNS. Recent computational methods for white blood cell nuclei segmentation: A comparative study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 173:1-14. [PMID: 31046984 DOI: 10.1016/j.cmpb.2019.03.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 02/05/2019] [Accepted: 03/04/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Leukaemia is a disease found worldwide; it is a type of cancer that originates in the bone marrow and is characterised by an abnormal proliferation of white blood cells (leukocytes). In order to correctly identify this abnormality, haematologists examine blood smears from patients. A diagnosis obtained by this method may be influenced by factors such as the experience and level of fatigue of the haematologist, resulting in non-standard reports and even errors. In the literature, several methods have been proposed that involve algorithms to diagnose this disease. However, no reviews or surveys have been conducted. This paper therefore presents an empirical investigation of computational methods focusing on the segmentation of leukocytes. METHODS In our study, 15 segmentation methods were evaluated using five public image databases: ALL-IDB2, BloodSeg, Leukocytes, JTSC Database and CellaVision. Following the standard methodology for literature evaluation, we conducted a pixel-level segmentation evaluation by comparing the segmented image with its corresponding ground truth. In order to identify the strengths and weaknesses of these methods, we performed an evaluation using six evaluation metrics: accuracy, specificity, precision, recall, kappa, Dice, and true positive rate. RESULTS The segmentation algorithms performed significantly differently for different image databases, and for each database, a different algorithm achieved the best results. Moreover, the two best methods achieved average accuracy values higher than 97%, with an excellent kappa index. Also, the average Dice index indicated that the similarity between the segmented leukocyte and its ground truth was higher than 0.85 for these two methods This result confirms the high level of similarity between these images but does not guarantee that a method has segmented all leukocyte nuclei. We also found that the method that performed best segmented only 58.44% of all leukocytes. CONCLUSIONS Of the techniques used to segment leukocytes, we note that clustering algorithms, the Otsu threshold, simple arithmetic operations and region growing are the approaches most widely used for this purpose. However, these computational methods have not yet overcome all the challenges posed by this problem.
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Affiliation(s)
| | | | | | | | | | - Fátima N S Medeiros
- Teleinformatics Engineering Department, Federal University of Ceará, Ceará, Brazil.
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Rehman A, Abbas N, Saba T, Rahman SIU, Mehmood Z, Kolivand H. Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech 2018; 81:1310-1317. [DOI: 10.1002/jemt.23139] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/25/2018] [Accepted: 09/01/2018] [Indexed: 11/11/2022]
Affiliation(s)
- Amjad Rehman
- College of Computer and Information SystemsAl Yamamah University Riyadh Saudi Arabia
| | - Naveed Abbas
- Department of Computer ScienceIslamia College University Peshawar Pakistan
| | - Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh Saudi Arabia
| | | | - Zahid Mehmood
- Department of Software EngineeringUniversity of Engineering and Technology Taxila Pakistan
| | - Hoshang Kolivand
- Department of Computer ScienceLiverpool John Moores University Liverpool United Kingdom
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Rawat J, Singh A, HS B, Virmani J, Devgun JS. Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2017.07.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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