1
|
Saxena P, Aggarwal SK, Sinha A, Saxena S, Singh AK. Review of computer-assisted diagnosis model to classify follicular lymphoma histology. Cell Biochem Funct 2024; 42:e4088. [PMID: 38973163 DOI: 10.1002/cbf.4088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/15/2024] [Accepted: 06/28/2024] [Indexed: 07/09/2024]
Abstract
The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.
Collapse
Affiliation(s)
- Pranshu Saxena
- School of Computer Science Engineering & Technology, Bennett University, Greater Noida, Uttar Pradesh, India
| | - Sahil Kumar Aggarwal
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Amit Sinha
- Department of Information Technology, ABES Engineering College, Ghaziabad, India
| | - Sandeep Saxena
- Greater Noida Institute of Technology, Greater Noida, India
| | | |
Collapse
|
2
|
Song Y, Zhang A, Zhou J, Luo Y, Lin Z, Zhou T. Overlapping cytoplasms segmentation via constrained multi-shape evolution for cervical cancer screening. Artif Intell Med 2024; 148:102756. [PMID: 38325933 DOI: 10.1016/j.artmed.2023.102756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 12/03/2023] [Accepted: 12/29/2023] [Indexed: 02/09/2024]
Abstract
Segmenting overlapping cytoplasms in cervical smear images is a clinically essential task for quantitatively measuring cell-level features to screen cervical cancer This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region Although shape prior-based models that compensate intensity deficiency by introducing prior shape information about cytoplasm are firmly established, they often yield visually implausible results, as they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm In this paper, we present an effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors We model local shape priors (cytoplasm-level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump-level) modeled by considering mutual shape constraints of cytoplasms in the clump We also constrain the resulting shape in each evolution to be in the built shape hypothesis set for further reducing implausible segmentation results We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results confirm its effectiveness.
Collapse
Affiliation(s)
- Youyi Song
- School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Ao Zhang
- School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Jinglin Zhou
- School of Philosophy, Fudan University, Shanghai, 200433, China
| | - Yu Luo
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China
| | - Zhizhe Lin
- School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
| | - Teng Zhou
- School of Cyberspace Security, Hainan University, Haikou, 570228, China.
| |
Collapse
|
3
|
Li Z, Li C, Luo X, Zhou Y, Zhu J, Xu C, Yang M, Wu Y, Chen Y. Toward Source-Free Cross Tissues Histopathological Cell Segmentation via Target-Specific Finetuning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2666-2677. [PMID: 37030826 DOI: 10.1109/tmi.2023.3263465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Recognition and quantitative analytics of histopathological cells are the golden standard for diagnosing multiple cancers. Despite recent advances in deep learning techniques that have been widely investigated for the automated segmentation of various types of histopathological cells, the heavy dependency on specific histopathological image types with sufficient supervised annotations, as well as the limited access to clinical data in hospitals, still pose significant challenges in the application of computer-aided diagnosis in pathology. In this paper, we focus on the model generalization of cell segmentation towards cross-tissue histopathological images. Remarkably, a novel target-specific finetuning-based self-supervised domain adaptation framework is proposed to transfer the cell segmentation model to unlabeled target datasets, without access to source datasets and annotations. When performed on the target unlabeled histopathological image set, the proposed method only needs to tune very few parameters of the pre-trained model in a self-supervised manner. Considering the morphological properties of pathological cells, we introduce two constraint terms at both local and global levels into this framework to access more reliable predictions. The proposed cross-domain framework is validated on three different types of histopathological tissues, showing promising performance in self-supervised cell segmentation. Additionally, the whole framework can be further applied to clinical tools in pathology without accessing the original training image data. The code and dataset are released at: https://github.com/NeuronXJTU/SFDA-CellSeg.
Collapse
|
4
|
Iqbal S, Qureshi AN, Alhussein M, Aurangzeb K, Kadry S. A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images. Biomimetics (Basel) 2023; 8:370. [PMID: 37622975 PMCID: PMC10452605 DOI: 10.3390/biomimetics8040370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/26/2023] Open
Abstract
The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model's guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model's superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.
Collapse
Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
| |
Collapse
|
5
|
Ghose S, Ju Y, McDonough E, Ho J, Karunamurthy A, Chadwick C, Cho S, Rose R, Corwin A, Surrette C, Martinez J, Williams E, Sood A, Al-Kofahi Y, Falo LD, Börner K, Ginty F. 3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging. Commun Biol 2023; 6:718. [PMID: 37468758 PMCID: PMC10356782 DOI: 10.1038/s42003-023-04991-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/11/2023] [Indexed: 07/21/2023] Open
Abstract
Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a workflow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fixed skin (stained with 18 biomarkers) from 12 donors aged between 32-72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show significantly shorter distances between immune cells and vascular endothelial cells (56 µm in 3D vs 108 µm in 2D). We also show 10-70% more T cells (total) within 30 µm of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.
Collapse
Affiliation(s)
- Soumya Ghose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yingnan Ju
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA
| | | | - Jonhan Ho
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | | | | | - Sanghee Cho
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Rachel Rose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Alex Corwin
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | | | - Jessica Martinez
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Eric Williams
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Anup Sood
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yousef Al-Kofahi
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Louis D Falo
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | - Katy Börner
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA.
| | - Fiona Ginty
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA.
| |
Collapse
|
6
|
Zhang E, Xie R, Bian Y, Wang J, Tao P, Zhang H, Jiang S. Cervical cell nuclei segmentation based on GC-UNet. Heliyon 2023; 9:e17647. [PMID: 37456010 PMCID: PMC10345258 DOI: 10.1016/j.heliyon.2023.e17647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023] Open
Abstract
Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as overlapping cells and blurred nuclei boundaries. This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation. At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge. A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained. Further, a decoder grounded in a global context attention block is employed to foster global feature interaction and refine the predicted masks.
Collapse
Affiliation(s)
- Enguang Zhang
- School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China
- Zhuhai College of Science and Technology, Zhuhai, China
| | - Rixin Xie
- School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China
| | - Yuxin Bian
- School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China
| | - Jiayan Wang
- School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China
| | - Pengyi Tao
- School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China
| | - Heng Zhang
- Faculty of Education, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shenlu Jiang
- School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China
| |
Collapse
|
7
|
Ram S, Tang W, Bell AJ, Pal R, Spencer C, Buschhaus A, Hatt CR, diMagliano MP, Rehemtulla A, Rodríguez JJ, Galban S, Galban CJ. Lung cancer lesion detection in histopathology images using graph-based sparse PCA network. Neoplasia 2023; 42:100911. [PMID: 37269818 DOI: 10.1016/j.neo.2023.100911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/17/2023] [Indexed: 06/05/2023]
Abstract
Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fβ-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.
Collapse
Affiliation(s)
- Sundaresh Ram
- Departments of Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Wenfei Tang
- Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexander J Bell
- Departments of Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ravi Pal
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Cara Spencer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Charles R Hatt
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA; Imbio LLC, Minneapolis, MN 55405, USA
| | - Marina Pasca diMagliano
- Departments of Surgery, and Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alnawaz Rehemtulla
- Departments of Radiology, and Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jeffrey J Rodríguez
- Departments of Electrical and Computer Engineering, and Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, USA
| | - Stefanie Galban
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Craig J Galban
- Departments of Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
8
|
Zheng X, Tang P, Ai L, Liu D, Zhang Y, Wang B. White blood cell detection using saliency detection and CenterNet: A two-stage approach. JOURNAL OF BIOPHOTONICS 2023; 16:e202200174. [PMID: 36101492 DOI: 10.1002/jbio.202200174] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/23/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
White blood cell (WBC) detection plays a vital role in peripheral blood smear analysis. However, cell detection remains a challenging task due to multi-cell adhesion, different staining and imaging conditions. Owing to the powerful feature extraction capability of deep learning, object detection methods based on convolutional neural networks (CNNs) have been widely applied in medical image analysis. Nevertheless, the CNN training is time-consuming and inaccuracy, especially for large-scale blood smear images, where most of the images are background. To address the problem, we propose a two-stage approach that treats WBC detection as a small salient object detection task. In the first saliency detection stage, we use the Itti's visual attention model to locate the regions of interest (ROIs), based on the proposed adaptive center-surround difference (ACSD) operator. In the second WBC detection stage, the modified CenterNet model is performed on ROI sub-images to obtain a more accurate localization and classification result of each WBC. Experimental results showed that our method exceeds the performance of several existing methods on two different data sets, and achieves a state-of-the-art mAP of over 98.8%.
Collapse
Affiliation(s)
- Xin Zheng
- School of Computer and Information, Anqing Normal University, Anqing, China
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing, China
| | - Pan Tang
- School of Computer and Information, Anqing Normal University, Anqing, China
| | - Liefu Ai
- School of Computer and Information, Anqing Normal University, Anqing, China
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing, China
| | - Deyang Liu
- School of Computer and Information, Anqing Normal University, Anqing, China
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing, China
| | - Youzhi Zhang
- School of Computer and Information, Anqing Normal University, Anqing, China
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing, China
| | - Boyang Wang
- School of Computer and Information, Anqing Normal University, Anqing, China
| |
Collapse
|
9
|
Saxena P, Goyal A. Computer-assisted grading of follicular lymphoma: a classification based on SVM, machine learning, and transfer learning approaches. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2162663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Pranshu Saxena
- I.K. Gujral Punjab Technical University, Jalandhar, India
| | - Anjali Goyal
- Department of Computer Applications, GNIMT, Ludhiana, India
| |
Collapse
|
10
|
Mukherjee S, Sarkar R, Manich M, Labruyere E, Olivo-Marin JC. Domain Adapted Multitask Learning for Segmenting Amoeboid Cells in Microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:42-54. [PMID: 36044485 DOI: 10.1109/tmi.2022.3203022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The method proposed in this paper is a robust combination of multi-task learning and unsupervised domain adaptation for segmenting amoeboid cells in microscopy. A highlight of this work is the manner in which the model's hyperparameters are estimated. The detriments of ad-hoc parameter estimation are well known, but this issue remains largely unaddressed in the context of CNN-based segmentation. Using a novel min-max formulation of the segmentation cost function our proposed method analytically estimates the model's hyperparameters, while simultaneously learning the CNN weights during training. This end-to-end framework provides a consolidated mechanism to harness the potential of multi-task learning to isolate and segment clustered cells from low contrast brightfield images, and it simultaneously leverages deep domain adaptation to segment fluorescent cells without explicit pixel-level re- annotation of the data. Experimental validations on multi-cellular images strongly suggest the effectiveness of the proposed technique, and our quantitative results show at least 15% and 10% improvement in cell segmentation on brightfield and fluorescence images respectively compared to contemporary supervised segmentation methods.
Collapse
|
11
|
Liang Y, Yin Z, Liu H, Zeng H, Wang J, Liu J, Che N. Weakly Supervised Deep Nuclei Segmentation With Sparsely Annotated Bounding Boxes for DNA Image Cytometry. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:785-795. [PMID: 34951851 DOI: 10.1109/tcbb.2021.3138189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Nuclei segmentation is an essential step in DNA ploidy analysis by image-based cytometry (DNA-ICM) which is widely used in cytopathology and allows an objective measurement of DNA content (ploidy). The routine fully supervised learning-based method requires often tedious and expensive pixel-wise labels. In this paper, we propose a novel weakly supervised nuclei segmentation framework which exploits only sparsely annotated bounding boxes, without any segmentation labels. The key is to integrate the traditional image segmentation and self-training into fully supervised instance segmentation. We first leverage the traditional segmentation to generate coarse masks for each box-annotated nucleus to supervise the training of a teacher model, which is then responsible for both the refinement of these coarse masks and pseudo labels generation of unlabeled nuclei. These pseudo labels and refined masks along with the original manually annotated bounding boxes jointly supervise the training of student model. Both teacher and student share the same architecture and especially the student is initialized by the teacher. We have extensively evaluated our method with both our DNA-ICM dataset and public cytopathological dataset. Without bells and whistles, our method outperforms all existing weakly supervised entries on both datasets. Code and our DNA-ICM dataset are publicly available at https://github.com/CVIU-CSU/Weakly-Supervised-Nuclei-Segmentation.
Collapse
|
12
|
Wu H, Pang KKY, Pang GKH, Au-Yeung RKH. A soft-computing based approach to overlapped cells analysis in histopathology images with genetic algorithm. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
13
|
Garg N, Sinha D, Yadav B, Gupta B, Gupta S, Miah S. ML-Based Texture and Wavelet Features Extraction Technique to Predict Gastric Mesothelioma Cancer. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1012684. [PMID: 35832854 PMCID: PMC9273447 DOI: 10.1155/2022/1012684] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022]
Abstract
Microsatellites are small, repetitive sequences found all across the human genome. Microsatellite instability is the phenomenon of variations in the length of microsatellites induced by the insertion or deletion of repeat units in tumor tissue (MSI). MSI-type stomach malignancy has distinct genetic phenotypes and clinic pathological characteristics, and the stability of microsatellites influences whether or not patients with gastric mesothelioma react to immunotherapy. As a result, determining MSI status prior to surgery is critical for developing treatment options for individuals with gastric cancer. Traditional MSI detection approaches need immunological histochemistry and genetic analysis, which adds to the expense and makes it difficult to apply to every patient in clinical practice. In this study, to predict the MSI status of gastric cancer patients, researchers used image feature extraction technology and a machine learning algorithm to evaluate high-resolution histopathology pictures of patients. 279 cases of raw data were obtained from the TCGA database, 442 samples were obtained after preprocessing and upsampling, and 445 quantitative image features, including first-order statistics of impressions, texture features, and wavelet features, were extracted from the histopathological images of each sample. To filter the characteristics and provide a prediction label (risk score) for MSI status of gastric cancer, Lasso regression was utilized. The predictive label's classification performance was evaluated using a logistic classification model, which was then coupled with the clinical data of each patient to create a customized nomogram for MSI status prediction using multivariate analysis.
Collapse
Affiliation(s)
- Neeraj Garg
- Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
| | | | - Babita Yadav
- School of Engineering and Technology, MVN University, India
| | - Bhoomi Gupta
- Department of Information Technology, Maharaja Agrasen Institute of Technology, Delhi, India
| | - Sachin Gupta
- School of Engineering and Technology, MVN University, India
| | - Shahajan Miah
- Department of EEE, Bangladesh University of Business and Technology (BUBT), Dhaka, Bangladesh
| |
Collapse
|
14
|
He W, Liu T, Han Y, Ming W, Du J, Liu Y, Yang Y, Wang L, Jiang Z, Wang Y, Yuan J, Cao C. A review: The detection of cancer cells in histopathology based on machine vision. Comput Biol Med 2022; 146:105636. [PMID: 35751182 DOI: 10.1016/j.compbiomed.2022.105636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/04/2022] [Accepted: 04/28/2022] [Indexed: 12/24/2022]
Abstract
Machine vision is being employed in defect detection, size measurement, pattern recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection methods are dominated by manual detection, which wastes time and manpower, and heavily relies on the pathologists' skill and work experience. Therefore, these manual detection approaches are not convenient for the inheritance of domain knowledge, and are not suitable for the rapid development of medical care in the future. The emergence of machine vision can iteratively update and learn the domain knowledge of cancer cell pathology detection to achieve automated, high-precision, and consistent detection. Consequently, this paper reviews the use of machine vision to detect cancer cells in histopathology images, as well as the benefits and drawbacks of various detection approaches. First, we review the application of image preprocessing and image segmentation in histopathology for the detection of cancer cells, and compare the benefits and drawbacks of different algorithms. Secondly, for the characteristics of histopathological cancer cell images, the research progress of shape, color and texture features and other methods is mainly reviewed. Furthermore, for the classification methods of histopathological cancer cell images, the benefits and drawbacks of traditional machine vision approaches and deep learning methods are compared and analyzed. Finally, the above research is discussed and forecasted, with the expected future development tendency serving as a guide for future research.
Collapse
Affiliation(s)
- Wenbin He
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Ting Liu
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongjie Han
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Wuyi Ming
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China.
| | - Jinguang Du
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yinxia Liu
- Laboratory Medicine of Dongguan Kanghua Hospital, Dongguan, 523808, China
| | - Yuan Yang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
| | - Leijie Wang
- School of Mechanical Engineering, Dongguan University of Technology Dongguan, 523808, China
| | - Zhiwen Jiang
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongqiang Wang
- Zhengzhou Coal Mining Machinery Group Co., Ltd, Zhengzhou, 450016, China
| | - Jie Yuan
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Chen Cao
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China
| |
Collapse
|
15
|
Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization. Int J Biomed Imaging 2022; 2022:3211793. [PMID: 35496641 PMCID: PMC9050319 DOI: 10.1155/2022/3211793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 02/08/2022] [Accepted: 03/31/2022] [Indexed: 11/17/2022] Open
Abstract
In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.
Collapse
|
16
|
Zhao M, Wang S, Shi F, Jia C, Sun X, Chen S. OUP accepted manuscript. Bioinformatics 2022; 38:i53-i59. [PMID: 35758798 PMCID: PMC9235483 DOI: 10.1093/bioinformatics/btac219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Motivation The presence of tumor cell clusters in pleural effusion may be a signal of cancer metastasis. The instance segmentation of single cell from cell clusters plays a pivotal role in cluster cell analysis. However, current cell segmentation methods perform poorly for cluster cells due to the overlapping/touching characters of clusters, multiple instance properties of cells, and the poor generalization ability of the models. Results In this article, we propose a contour constraint instance segmentation framework (CC framework) for cluster cells based on a cluster cell combination enhancement module. The framework can accurately locate each instance from cluster cells and realize high-precision contour segmentation under a few samples. Specifically, we propose the contour attention constraint module to alleviate over- and under-segmentation among individual cell-instance boundaries. In addition, to evaluate the framework, we construct a pleural effusion cluster cell dataset including 197 high-quality samples. The quantitative results show that the numeric result of APmask is > 90%, a more than 10% increase compared with state-of-the-art semantic segmentation algorithms. From the qualitative results, we can observe that our method rarely has segmentation errors.
Collapse
Affiliation(s)
- Meng Zhao
- To whom correspondence should be addressed. E-mail: or
| | - Siyu Wang
- Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), The Key Laboratory of Computer Vision and System (Ministry of Education), and the School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Fan Shi
- Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), The Key Laboratory of Computer Vision and System (Ministry of Education), and the School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Chen Jia
- Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), The Key Laboratory of Computer Vision and System (Ministry of Education), and the School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Xuguo Sun
- School of Medical Laboratory, Tianjin Medical University, Tianjin 300204, China
| | - Shengyong Chen
- Engineering Research Center of Learning-Based Intelligent System (Ministry of Education), The Key Laboratory of Computer Vision and System (Ministry of Education), and the School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| |
Collapse
|
17
|
Arunachalam P, Venkatakrishnan P, Janakiraman N, Sangeetha S. Detection of Structure Characteristics and Its Discontinuity Based Field Programmable Gate Array Processor in Cancer Cell by Wavelet Transform. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Digital clinical histopathology is one of the crucial techniques for precise cancer cell diagnosing in modern medicine. The Synovial Sarcoma (SS) cancer cell patterns seem to be a spindle shaped cell (SSC) structure and it is very difficult to identify the exact oval shaped cell
structure through pathologist’s eye perception. Meanwhile, there is necessitating for monitoring and securing the successful and effective image data processing in the the huge network data which is also a complex one. A field programmable Gate Array (FPGA) was regarded as a necessary
one for this. In this work, based on FPGA a Cancer Cell classification is made for the regulation and execution. Hence, mathematically the SSC regularity structures and its discontinuities are measured by the holder exponent (HE) function. In this research work, HE values have been
determined by Wavelet Transform Modulus Maxima (WTMM) and Wavelet Leader (WL) methods with basis function of Haar wavelet based on FPGA Processor. The quantitative parameters such as Mean of Asymptotic Discontinuity (MAD), Mean of Removable Discontinuity (MRD) and Number of Discontinuity Points
(NDPs) have been considered to determine the prediction of discontinuity detection between WTMM and WL methods. With the help of receiver operating characteristics (ROC) curve, the significant difference of discontinuity detection performance between both the methods has been analyzed. From
the experimental results, it is clear that the WL method is more practically feasible and it gives satisfactory performance, in terms of sensitivity and specificity percentage values, which are 80.56% and 59.46%, respectively in the blue color components of the SNR 20 dB noise image.
Collapse
Affiliation(s)
- P. Arunachalam
- Electronics and Communication Engineering Department, KLN College of Engineering-Madurai, Affiliated to Centre for Research Anna University-Chennai 630612, Tamilnadu, India
| | - P. Venkatakrishnan
- Electronics and Communication Engineering Department, CMR Technical Campus, Telangana 501401, India
| | - N. Janakiraman
- Electronics and Communication Engineering Department, KLN College of Engineering-Madurai, Affiliated to Centre for Research Anna University-Chennai 630612, Tamilnadu, India
| | - S. Sangeetha
- Electrical and Electronics Engineering Department, CMR College of Engineering & Technology, Telangana 501401, India
| |
Collapse
|
18
|
Verma R, Kumar N, Patil A, Kurian NC, Rane S, Graham S, Vu QD, Zwager M, Raza SEA, Rajpoot N, Wu X, Chen H, Huang Y, Wang L, Jung H, Brown GT, Liu Y, Liu S, Jahromi SAF, Khani AA, Montahaei E, Baghshah MS, Behroozi H, Semkin P, Rassadin A, Dutande P, Lodaya R, Baid U, Baheti B, Talbar S, Mahbod A, Ecker R, Ellinger I, Luo Z, Dong B, Xu Z, Yao Y, Lv S, Feng M, Xu K, Zunair H, Hamza AB, Smiley S, Yin TK, Fang QR, Srivastava S, Mahapatra D, Trnavska L, Zhang H, Narayanan PL, Law J, Yuan Y, Tejomay A, Mitkari A, Koka D, Ramachandra V, Kini L, Sethi A. MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3413-3423. [PMID: 34086562 DOI: 10.1109/tmi.2021.3085712] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.
Collapse
|
19
|
Vahadane A, B A, Majumdar S. Dual Encoder Attention U-net for Nuclei Segmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3205-3208. [PMID: 34891923 DOI: 10.1109/embc46164.2021.9630037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Nuclei segmentation in whole slide images (WSIs) stained with Hematoxylin and Eosin (H&E) dye, is a key step in computational pathology which aims to automate the laborious process of manual counting and segmentation. Nuclei segmentation is a challenging problem that involves challenges such as touching nuclei resolution, small-sized nuclei, size, and shape variations. With the advent of deep learning, convolution neural networks (CNNs) have shown a powerful ability to extract effective representations from microscopic H&E images. We propose a novel dual encoder Attention U-net (DEAU) deep learning architecture and pseudo hard attention gating mechanism, to enhance the attention to target instances. We added a new secondary encoder to the attention U-net to capture the best attention for a given input. Since H captures nuclei information, we propose a stain-separated H channel as input to the secondary encoder. The role of the secondary encoder is to transform attention prior to different spatial resolutions while learning significant attention information. The proposed DEAU performance was evaluated on three publicly available H&E data sets for nuclei segmentation from different research groups. Experimental results show that our approach outperforms other attention-based approaches for nuclei segmentation.
Collapse
|
20
|
Wang J, Zhang M, Zhang J, Wang Y, Gahlmann A, Acton ST. Graph-Theoretic Post-Processing of Segmentation With Application to Dense Biofilms. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:8580-8594. [PMID: 34613914 PMCID: PMC9159353 DOI: 10.1109/tip.2021.3116792] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recent deep learning methods have provided successful initial segmentation results for generalized cell segmentation in microscopy. However, for dense arrangements of small cells with limited ground truth for training, the deep learning methods produce both over-segmentation and under-segmentation errors. Post-processing attempts to balance the trade-off between the global goal of cell counting for instance segmentation, and local fidelity to the morphology of identified cells. The need for post-processing is especially evident for segmenting 3D bacterial cells in densely-packed communities called biofilms. A graph-based recursive clustering approach, m-LCuts, is proposed to automatically detect collinearly structured clusters and applied to post-process unsolved cells in 3D bacterial biofilm segmentation. Construction of outlier-removed graphs to extract the collinearity feature in the data adds additional novelty to m-LCuts. The superiority of m-LCuts is observed by the evaluation in cell counting with over 90% of cells correctly identified, while a lower bound of 0.8 in terms of average single-cell segmentation accuracy is maintained. This proposed method does not need manual specification of the number of cells to be segmented. Furthermore, the broad adaptation for working on various applications, with the presence of data collinearity, also makes m-LCuts stand out from the other approaches.
Collapse
|
21
|
Li X, Xu Z, Shen X, Zhou Y, Xiao B, Li TQ. Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN. Curr Oncol 2021; 28:3585-3601. [PMID: 34590614 PMCID: PMC8482136 DOI: 10.3390/curroncol28050307] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/06/2021] [Accepted: 09/12/2021] [Indexed: 01/16/2023] Open
Abstract
Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN architecture for the detection of abnormal cervical cells in cytology images from a cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cervical image dataset of "Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using traditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.
Collapse
Affiliation(s)
- Xia Li
- Institute of Information Engineering, China Jiliang University, Hangzhou 310018, China; (X.L.); (Z.X.); (X.S.); (Y.Z.); (B.X.)
| | - Zhenhao Xu
- Institute of Information Engineering, China Jiliang University, Hangzhou 310018, China; (X.L.); (Z.X.); (X.S.); (Y.Z.); (B.X.)
| | - Xi Shen
- Institute of Information Engineering, China Jiliang University, Hangzhou 310018, China; (X.L.); (Z.X.); (X.S.); (Y.Z.); (B.X.)
| | - Yongxia Zhou
- Institute of Information Engineering, China Jiliang University, Hangzhou 310018, China; (X.L.); (Z.X.); (X.S.); (Y.Z.); (B.X.)
| | - Binggang Xiao
- Institute of Information Engineering, China Jiliang University, Hangzhou 310018, China; (X.L.); (Z.X.); (X.S.); (Y.Z.); (B.X.)
| | - Tie-Qiang Li
- Institute of Information Engineering, China Jiliang University, Hangzhou 310018, China; (X.L.); (Z.X.); (X.S.); (Y.Z.); (B.X.)
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, S-17177 Stockholm, Sweden
- Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital, S-14186 Stockholm, Sweden
| |
Collapse
|
22
|
Mahmood T, Owais M, Noh KJ, Yoon HS, Koo JH, Haider A, Sultan H, Park KR. Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine. J Pers Med 2021; 11:515. [PMID: 34199932 PMCID: PMC8226747 DOI: 10.3390/jpm11060515] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/20/2021] [Accepted: 06/03/2021] [Indexed: 11/16/2022] Open
Abstract
Accurate nuclear segmentation in histopathology images plays a key role in digital pathology. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. However, it is a very challenging task because of the different types of nuclei, large intraclass variations, and diverse cell morphologies. Consequently, the manual inspection of such images under high-resolution microscopes is tedious and time-consuming. Alternatively, artificial intelligence (AI)-based automated techniques, which are fast and robust, and require less human effort, can be used. Recently, several AI-based nuclear segmentation techniques have been proposed. They have shown a significant performance improvement for this task, but there is room for further improvement. Thus, we propose an AI-based nuclear segmentation technique in which we adopt a new nuclear segmentation network empowered by residual skip connections to address this issue. Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). The results show that our proposed technique achieves an aggregated Jaccard index (AJI) of 0.6794, Dice coefficient of 0.8084, and F1-measure of 0.8547 on TCGA dataset, and an AJI of 0.7332, Dice coefficient of 0.8441, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset. These values are higher than those of the state-of-the-art methods.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea; (T.M.); (M.O.); (K.J.N.); (H.S.Y.); (J.H.K.); (A.H.); (H.S.)
| |
Collapse
|
23
|
Qu H, Wu P, Huang Q, Yi J, Yan Z, Li K, Riedlinger GM, De S, Zhang S, Metaxas DN. Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3655-3666. [PMID: 32746112 DOI: 10.1109/tmi.2020.3002244] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise annotations for fully supervised training. To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled. The framework consists of two learning stages. In the first stage, we design a semi-supervised strategy to learn a detection model from partially labeled nuclei locations. Specifically, an extended Gaussian mask is designed to train an initial model with partially labeled data. Then, self-training with background propagation is proposed to make use of the unlabeled regions to boost nuclei detection and suppress false positives. In the second stage, a segmentation model is trained from the detected nuclei locations in a weakly-supervised fashion. Two types of coarse labels with complementary information are derived from the detected points and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized in training to further refine the model without introducing extra computational complexity during inference. The proposed method is extensively evaluated on two nuclei segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.
Collapse
|
24
|
Mahmood T, Owais M, Noh KJ, Yoon HS, Haider A, Sultan H, Park KR. Artificial Intelligence-based Segmentation of Nuclei in Multi-organ Histopathology Images: Model Development and Validation (Preprint). JMIR Med Inform 2020. [DOI: 10.2196/24394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
25
|
Kumar N, Verma R, Anand D, Zhou Y, Onder OF, Tsougenis E, Chen H, Heng PA, Li J, Hu Z, Wang Y, Koohbanani NA, Jahanifar M, Tajeddin NZ, Gooya A, Rajpoot N, Ren X, Zhou S, Wang Q, Shen D, Yang CK, Weng CH, Yu WH, Yeh CY, Yang S, Xu S, Yeung PH, Sun P, Mahbod A, Schaefer G, Ellinger I, Ecker R, Smedby O, Wang C, Chidester B, Ton TV, Tran MT, Ma J, Do MN, Graham S, Vu QD, Kwak JT, Gunda A, Chunduri R, Hu C, Zhou X, Lotfi D, Safdari R, Kascenas A, O'Neil A, Eschweiler D, Stegmaier J, Cui Y, Yin B, Chen K, Tian X, Gruening P, Barth E, Arbel E, Remer I, Ben-Dor A, Sirazitdinova E, Kohl M, Braunewell S, Li Y, Xie X, Shen L, Ma J, Baksi KD, Khan MA, Choo J, Colomer A, Naranjo V, Pei L, Iftekharuddin KM, Roy K, Bhattacharjee D, Pedraza A, Bueno MG, Devanathan S, Radhakrishnan S, Koduganty P, Wu Z, Cai G, Liu X, Wang Y, Sethi A. A Multi-Organ Nucleus Segmentation Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1380-1391. [PMID: 31647422 PMCID: PMC10439521 DOI: 10.1109/tmi.2019.2947628] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.
Collapse
|
26
|
Huang J, Wang T, Zheng D, He Y. Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm. Bioengineered 2020; 11:484-501. [PMID: 32279589 PMCID: PMC7161549 DOI: 10.1080/21655979.2020.1747834] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results.
Collapse
Affiliation(s)
- Jinjie Huang
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin, China.,School of Computer Science, Harbin University of Science and Technology, Harbin, China
| | - Tao Wang
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin, China.,School of Computer Science, Harbin University of Science and Technology, Harbin, China.,Network and Education Technology Center, Harbin University of Commerce, Harbin, China
| | - Dequan Zheng
- Network and Education Technology Center, Harbin University of Commerce, Harbin, China
| | - Yongjun He
- School of Computer Science, Harbin University of Science and Technology, Harbin, China
| |
Collapse
|
27
|
Jung H, Lodhi B, Kang J. An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images. BMC Biomed Eng 2019; 1:24. [PMID: 32903361 PMCID: PMC7422516 DOI: 10.1186/s42490-019-0026-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 09/02/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.
Collapse
Affiliation(s)
- Hwejin Jung
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Bilal Lodhi
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
- Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Republic of Korea
| |
Collapse
|
28
|
Wang S, Yang DM, Rong R, Zhan X, Xiao G. Pathology Image Analysis Using Segmentation Deep Learning Algorithms. THE AMERICAN JOURNAL OF PATHOLOGY 2019; 189:1686-1698. [PMID: 31199919 PMCID: PMC6723214 DOI: 10.1016/j.ajpath.2019.05.007] [Citation(s) in RCA: 163] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 05/01/2019] [Accepted: 05/09/2019] [Indexed: 12/16/2022]
Abstract
With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and accurately remain significant challenges. Recently, deep learning algorithms have shown great promise in pathology image analysis, such as in tumor region identification, metastasis detection, and patient prognosis. Many machine learning algorithms, including convolutional neural networks, have been proposed to automatically segment pathology images. Among these algorithms, segmentation deep learning algorithms such as fully convolutional networks stand out for their accuracy, computational efficiency, and generalizability. Thus, deep learning-based pathology image segmentation has become an important tool in WSI analysis. In this review, the pathology image segmentation process using deep learning algorithms is described in detail. The goals are to provide quick guidance for implementing deep learning into pathology image analysis and to provide some potential ways of further improving segmentation performance. Although there have been previous reviews on using machine learning methods in digital pathology image analysis, this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.
Collapse
Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.
| |
Collapse
|
29
|
Sarwar A, Sheikh AA, Manhas J, Sharma V. Segmentation of cervical cells for automated screening of cervical cancer: a review. Artif Intell Rev 2019. [DOI: 10.1007/s10462-019-09735-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
30
|
Fan H, Zhang F, Xi L, Li Z, Liu G, Xu Y. LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. JOURNAL OF BIOPHOTONICS 2019; 12:e201800488. [PMID: 30891934 DOI: 10.1002/jbio.201800488] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 03/15/2019] [Accepted: 03/17/2019] [Indexed: 06/09/2023]
Abstract
Digital pathology and microscope image analysis is widely used in comprehensive studies of cell morphology. Identification and analysis of leukocytes in blood smear images, acquired from bright field microscope, are vital for diagnosing many diseases such as hepatitis, leukaemia and acquired immune deficiency syndrome (AIDS). The major challenge for robust and accurate identification and segmentation of leukocyte in blood smear images lays in the large variations of cell appearance such as size, colour and shape of cells, the adhesion between leukocytes (white blood cells, WBCs) and erythrocytes (red blood cells, RBCs), and the emergence of substantial dyeing impurities in blood smear images. In this paper, an end-to-end leukocyte localization and segmentation method is proposed, named LeukocyteMask, in which pixel-level prior information is utilized for supervisor training of a deep convolutional neural network, which is then employed to locate the region of interests (ROI) of leukocyte, and finally segmentation mask of leukocyte is obtained based on the extracted ROI by forward propagation of the network. Experimental results validate the effectiveness of the propose method and both the quantitative and qualitative comparisons with existing methods indicate that LeukocyteMask achieves a state-of-the-art performance for the segmentation of leukocyte in terms of robustness and accuracy .
Collapse
Affiliation(s)
- Haoyi Fan
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Fengbin Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Liang Xi
- 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
- Fujian Province Collaborative Innovation Center of Traditional Chinese Medicine Health Management 2011, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Guanghai Liu
- College of Computer Science and Information Technology, Guangxi Normal University, Guilin, China
| | - Yong Xu
- Bio-Computing Research Center, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| |
Collapse
|
31
|
Xu J, Gong L, Wang G, Lu C, Gilmore H, Zhang S, Madabhushi A. Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images. J Med Imaging (Bellingham) 2019; 6:017501. [PMID: 30840729 DOI: 10.1117/1.jmi.6.1.017501] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 01/07/2019] [Indexed: 11/14/2022] Open
Abstract
Automated detection and segmentation of nuclei from high-resolution histopathological images is a challenging problem owing to the size and complexity of digitized histopathologic images. In the context of breast cancer, the modified Bloom-Richardson Grading system is highly correlated with the morphological and topological nuclear features are highly correlated with Modified Bloom-Richardson grading. Therefore, to develop a computer-aided prognosis system, automated detection and segmentation of nuclei are critical prerequisite steps. We present a method for automated detection and segmentation of breast cancer nuclei named a convolutional neural network initialized active contour model with adaptive ellipse fitting (CoNNACaeF). The CoNNACaeF model is able to detect and segment nuclei simultaneously, which consist of three different modules: convolutional neural network (CNN) for accurate nuclei detection, (2) region-based active contour (RAC) model for subsequent nuclear segmentation based on the initial CNN-based detection of nuclear patches, and (3) adaptive ellipse fitting for overlapping solution of clumped nuclear regions. The performance of the CoNNACaeF model is evaluated on three different breast histological data sets, comprising a total of 257 H&E-stained images. The model is shown to have improved detection accuracy of F-measure 80.18%, 85.71%, and 80.36% and average area under precision-recall curves (AveP) 77%, 82%, and 74% on a total of 3 million nuclei from 204 whole slide images from three different datasets. Additionally, CoNNACaeF yielded an F-measure at 74.01% and 85.36%, respectively, for two different breast cancer datasets. The CoNNACaeF model also outperformed the three other state-of-the-art nuclear detection and segmentation approaches, which are blue ratio initialized local region active contour, iterative radial voting initialized local region active contour, and maximally stable extremal region initialized local region active contour models.
Collapse
Affiliation(s)
- Jun Xu
- Nanjing University of Information Science and Technology, Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing, China
| | - Lei Gong
- Nanjing University of Information Science and Technology, Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing, China
| | - Guanhao Wang
- Nanjing University of Information Science and Technology, Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing, China
| | - Cheng Lu
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Hannah Gilmore
- University Hospitals Case Medical Center, Case Western Reserve University, Institute for Pathology, Cleveland, Ohio, United States
| | - Shaoting Zhang
- University of North Carolina at Charlotte, Department of Computer Science, Charlotte, North Carolina, United States
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, United States
| |
Collapse
|
32
|
Automatic cell nuclei segmentation and classification of cervical Pap smear images. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.09.008] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
33
|
Lanng MB, Møller CB, Andersen ASH, Pálsdóttir ÁA, Røge R, Østergaard LR, Jørgensen AS. Quality assessment of Ki67 staining using cell line proliferation index and stain intensity features. Cytometry A 2018; 95:381-388. [PMID: 30556331 DOI: 10.1002/cyto.a.23683] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 11/07/2022]
Abstract
Breast cancer is the most frequent cancer among women worldwide. Ki67 can be used as an immunohistochemical pseudo marker for cell proliferation to determine how aggressive the cancer is and thereby the treatment of the patient. No standard Ki67 staining protocol exists, resulting in inter-laboratory stain variability. Therefore, it is important to determine the quality control of a staining protocol to ensure correct diagnosis and treatment of patients. Currently, quality control is performed by the organization NordiQC that use an expert panel-based qualitative assessment system. However, no objective method exists to determine the quality of a staining protocol. In this study, we propose an algorithm, to objectively assess staining quality from segmented cell nuclei structures extracted from cell lines. The cell nuclei were classified into either Ki67 positive or negative to determine the Ki67 proliferation index within the cell lines. A Ki67 stain quality model based on ordinal logistic regression was developed to determine the quality of a staining protocol from features extracted from the segmented cell nuclei in the cell lines. The algorithm was able to segment and classify Ki67 positive cell nuclei with a sensitivity and positive predictive value (PPV) of 0.90 and 0.94 and Ki67 negative cell nuclei with a sensitivity and PPV of 0.78 and 0.78. The mean difference between a manual and automatic Ki67 proliferation index was -0.003 with a standard deviation of 0.056. The ordinal logistic regression model found that the stain intensity for both the Ki67 positive and Ki67 negative cell nuclei were statistically significant as parameters determining the stain quality from the cell line cores. The framework shows great promise for using cell nuclei information from cell lines to predict the staining quality of staining protocols. © 2018 International Society for Advancement of Cytometry.
Collapse
Affiliation(s)
- Mathias Buus Lanng
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Cecilie Brochdorff Møller
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Anne-Sofie Hendrup Andersen
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Ásgerður Arna Pálsdóttir
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Rasmus Røge
- Institute of Pathology, Aalborg University Hospital, Denmark.,The Department of Clinical Medicine, Aalborg University, Denmark
| | - Lasse Riis Østergaard
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Alex Skovsbo Jørgensen
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| |
Collapse
|
34
|
Li X, Plataniotis KN. Novel chromaticity similarity based color texture descriptor for digital pathology image analysis. PLoS One 2018; 13:e0206996. [PMID: 30419049 PMCID: PMC6231632 DOI: 10.1371/journal.pone.0206996] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 10/23/2018] [Indexed: 11/18/2022] Open
Abstract
Pathology images are color in nature due to the use of chemical staining in biopsy examination. Aware of the high color diagnosticity in pathology images, this work introduces a compact rotation-invariant texture descriptor, named quantized diagnostic counter-color pattern (QDCP), for digital pathology image understanding. On the basis of color similarity quantified by the inner product of unit-length color vectors, local counter-color textons are indexed first. Then the underlined distribution of QDCP indexes is estimated by an image-wise histogram. Since QDCP is computed based on color difference directly, it is robust to small color variation usually observed in pathology images. This study also discusses QDCP's extraction, parameter settings, and feature fusion techniques in a generic pathology image analysis pipeline, and introduces two more descriptors QDCP-LBP and QDCP/LBP. Experimentation on public pathology image sets suggests that the introduced color texture descriptors, especially QDCP-LBP, outperform prior color texture features in terms of strong descriptive power, low computational complexity, and high adaptability to different image sets.
Collapse
Affiliation(s)
- Xingyu Li
- Multimedia Lab, The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
| | - Konstantinos N. Plataniotis
- Multimedia Lab, The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
35
|
William W, Ware A, Basaza-Ejiri AH, Obungoloch J. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 164:15-22. [PMID: 30195423 DOI: 10.1016/j.cmpb.2018.05.034] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 04/17/2018] [Accepted: 05/29/2018] [Indexed: 05/24/2023]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis and classification of a cancer type can help facilitate the subsequent clinical management of the patient. Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. To that end, automated diagnosis and classification of cervical cancer from pap-smear images has become a necessity as it enables accurate, reliable and timely analysis of the condition's progress. This paper presents an overview of the state of the art as articulated in prominent recent publications focusing on automated detection of cervical cancer from pap-smear images. METHODS The survey reviews publications on applications of image analysis and machine learning in automated diagnosis and classification of cervical cancer from pap-smear images spanning 15 years. The survey reviews 30 journal papers obtained electronically through four scientific databases (Google Scholar, Scopus, IEEE and Science Direct) searched using three sets of keywords: (1) segmentation, classification, cervical cancer; (2) medical imaging, machine learning, pap-smear; (3) automated system, classification, pap-smear. RESULTS Most of the existing algorithms facilitate an accuracy of nearly 93.78% on an open pap-smear data set, segmented using CHAMP digital image software. K-nearest-neighbors and support vector machines algorithms have been reported to be excellent classifiers for cervical images with accuracies of over 99.27% and 98.5% respectively when applied to a 2-class classification problem (normal or abnormal). CONCLUSION The reviewed papers indicate that there are still weaknesses in the available techniques that result in low accuracy of classification in some classes of cells. Moreover, most of the existing algorithms work either on single or on multiple cervical smear images. This accuracy can be increased by varying various parameters such as the features to be extracted, improvement in noise removal, using hybrid segmentation and classification techniques such of multi-level classifiers. Combining K-nearest-neighbors algorithm with other algorithm(s) such as support vector machines, pixel level classifications and including statistical shape models can also improve performance. Further, most of the developed classifiers are tested on accurately segmented images using commercially available software such as CHAMP software. There is thus a deficit of evidence that these algorithms will work in clinical settings found in developing countries (where 85% of cervical cancer incidences occur) that lack sufficient trained cytologists and the funds to buy the commercial segmentation software.
Collapse
Affiliation(s)
- Wasswa William
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Uganda.
| | - Andrew Ware
- Faculty of Computing, Engineering and Science, University of South Wales, UK
| | | | - Johnes Obungoloch
- Department of Biomedical Sciences and Engineering, Mbarara University of Science and Technology, Uganda
| |
Collapse
|
36
|
Microscopic malaria parasitemia diagnosis and grading on benchmark datasets. Microsc Res Tech 2018; 81:1042-1058. [DOI: 10.1002/jemt.23071] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Revised: 04/23/2018] [Accepted: 05/10/2018] [Indexed: 12/16/2022]
|
37
|
Salvi M, Molinari F. Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images. Biomed Eng Online 2018; 17:89. [PMID: 29925379 PMCID: PMC6011253 DOI: 10.1186/s12938-018-0518-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 06/12/2018] [Indexed: 02/04/2023] Open
Abstract
Background Accurate nuclei detection and segmentation in histological images is essential for many clinical purposes. While manual annotations are time-consuming and operator-dependent, full automated segmentation remains a challenging task due to the high variability of cells intensity, size and morphology. Most of the proposed algorithms for the automated segmentation of nuclei were designed for specific organ or tissues. Results The aim of this study was to develop and validate a fully multiscale method, named MANA (Multiscale Adaptive Nuclei Analysis), for nuclei segmentation in different tissues and magnifications. MANA was tested on a dataset of H&E stained tissue images with more than 59,000 annotated nuclei, taken from six organs (colon, liver, bone, prostate, adrenal gland and thyroid) and three magnifications (10×, 20×, 40×). Automatic results were compared with manual segmentations and three open-source software designed for nuclei detection. For each organ, MANA obtained always an F1-score higher than 0.91, with an average F1 of 0.9305 ± 0.0161. The average computational time was about 20 s independently of the number of nuclei to be detected (anyway, higher than 1000), indicating the efficiency of the proposed technique. Conclusion To the best of our knowledge, MANA is the first fully automated multi-scale and multi-tissue algorithm for nuclei detection. Overall, the robustness and versatility of MANA allowed to achieve, on different organs and magnifications, performances in line or better than those of state-of-art algorithms optimized for single tissues.
Collapse
Affiliation(s)
- Massimo Salvi
- Biolab, Department of Electronics and Telecomunications, Politecnico di Torino, 10129, Turin, Italy.
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecomunications, Politecnico di Torino, 10129, Turin, Italy
| |
Collapse
|
38
|
Kharma N, Mazhurin A, Saigol K, Sabahi F. Adaptable image segmentation via simple pixel classification. Comput Intell 2018. [DOI: 10.1111/coin.12173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Nawwaf Kharma
- Department of Electrical and Computer Engineering; Concordia University; Montréal QC Canada
| | | | - Kamil Saigol
- Institute for Robotics and Intelligent Machines; Georgia Institute of Technology; Atlanta GA USA
| | - Farzad Sabahi
- Department of Electrical and Computer Engineering; Concordia University; Montréal QC Canada
| |
Collapse
|
39
|
Jordan J, Goldstein JS, Jaye DL, Gurcan M, Flowers CR, Cooper LAD. Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies. JCO Clin Cancer Inform 2018; 2. [PMID: 30637363 DOI: 10.1200/cci.17.00039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Purpose Lymphoid malignancies are remarkably heterogeneous, with variations in outcomes and clinical, biologic, and histologic presentation complicating classification according to the World Health Organization guidelines. Incorrect classification of lymphoid neoplasms can result in suboptimal therapeutic strategies for individual patients and confound the interpretation of clinical trials involving personalized, class-based treatments. This review discusses the potential role of pathology informatics in improving the classification accuracy and objectivity for lymphoid malignancies. Design We identified peer-reviewed publications examining pathology informatics approaches for the classification of lymphoid malignancies, reviewed developments in the lymphoma classification systems, and summarized computational methods for pathologic assessment that can impact practice. Results Computer-assisted pathology image analysis algorithms in lymphoma most commonly have been applied to follicular lymphoma to address biologic heterogeneity and subjectivity in the process of classification. Conclusion Objective methods are available to assist pathologists in lymphoma classification and grading, and have been demonstrated to provide measurable benefits in specific contexts. Future validation and extension of these approaches will require datasets that link high resolution pathology images available for image analysis algorithms with clinical variables and follow up outcomes.
Collapse
Affiliation(s)
- Jacob Jordan
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Jordan S Goldstein
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - David L Jaye
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Metin Gurcan
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Christopher R Flowers
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| | - Lee A D Cooper
- Emory University School of Medicine, Atlanta, GA; Metin Gurcan, Ohio State University, Columbus, OH
| |
Collapse
|
40
|
Zheng X, Wang Y, Wang G, Liu J. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 2018; 107:55-71. [PMID: 29425969 DOI: 10.1016/j.micron.2018.01.010] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 01/25/2018] [Accepted: 01/26/2018] [Indexed: 10/18/2022]
Abstract
A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets.
Collapse
Affiliation(s)
- Xin Zheng
- The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, School of Computer and Information, Anqing Normal University, Anqing 246133, China.
| | - Yong Wang
- Department of Computer Science and Technology, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Guoyou Wang
- National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Jianguo Liu
- National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
| |
Collapse
|
41
|
Xie Y, Xing F, Shi X, Kong X, Su H, Yang L. Efficient and robust cell detection: A structured regression approach. Med Image Anal 2018; 44:245-254. [PMID: 28797548 PMCID: PMC6051760 DOI: 10.1016/j.media.2017.07.003] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Revised: 02/22/2017] [Accepted: 07/21/2017] [Indexed: 10/19/2022]
Abstract
Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and large variations in cell sizes and shapes. In addition, the ever-increasing amount of available datasets and the high resolution of whole-slice scanned images pose a further demand for efficient processing algorithms. In this paper, we present a novel structured regression model based on a proposed fully residual convolutional neural network for efficient cell detection. For each testing image, our model learns to produce a dense proximity map that exhibits higher responses at locations near cell centers. Our method only requires a few training images with weak annotations (just one dot indicating the cell centroids). We have extensively evaluated our method using four different datasets, covering different microscopy staining methods (e.g., H & E or Ki-67 staining) or image acquisition techniques (e.g., bright-filed image or phase contrast). Experimental results demonstrate the superiority of our method over existing state of the art methods in terms of both detection accuracy and running time.
Collapse
Affiliation(s)
- Yuanpu Xie
- Department of Biomedical Engineering, University of Florida, FL 32611 USA.
| | - Fuyong Xing
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Xiaoshuang Shi
- Department of Biomedical Engineering, University of Florida, FL 32611 USA
| | - Xiangfei Kong
- School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Drive 637553 Singapore
| | - Hai Su
- Department of Biomedical Engineering, University of Florida, FL 32611 USA
| | - Lin Yang
- Department of Biomedical Engineering, University of Florida, FL 32611 USA; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.
| |
Collapse
|
42
|
Lee HG, Lee SC. Nucleus Segmentation Using Gaussian Mixture based Shape Models. IEEE J Biomed Health Inform 2018; 22:235-243. [DOI: 10.1109/jbhi.2017.2700518] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
43
|
Wang Y, Wang C, Zhang Z. Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information. J Microsc 2017; 270:188-199. [PMID: 29280132 DOI: 10.1111/jmi.12673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 10/01/2017] [Accepted: 11/27/2017] [Indexed: 11/28/2022]
Abstract
Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively.
Collapse
Affiliation(s)
- Y Wang
- School of Mechanical Engineering and Automation, Robotics Institute, Beihang University, Beijing, China
| | - C Wang
- School of Mechanical Engineering and Automation, Robotics Institute, Beihang University, Beijing, China
| | - Z Zhang
- Université de Bordeaux & CNRS, LOMA, Talence, France
| |
Collapse
|
44
|
Ahmad A, Asif A, Rajpoot N, Arif M, Minhas FUAA. Correlation Filters for Detection of Cellular Nuclei in Histopathology Images. J Med Syst 2017; 42:7. [DOI: 10.1007/s10916-017-0863-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 11/13/2017] [Indexed: 12/13/2022]
|
45
|
Serin F, Erturkler M, Gul M. A novel overlapped nuclei splitting algorithm for histopathological images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 151:57-70. [PMID: 28947006 DOI: 10.1016/j.cmpb.2017.08.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 06/27/2017] [Accepted: 08/14/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Nuclei segmentation is a common process for quantitative analysis of histopathological images. However, this process generally results in overlapping of nuclei due to the nature of images, the sample preparation and staining, and image acquisition processes as well as insufficiency of 2D histopathological images to represent 3D characteristics of tissues. We present a novel algorithm to split overlapped nuclei. METHODS The histopathological images are initially segmented by K-Means segmentation algorithm. Then, nuclei cluster are converted to binary image. The overlapping is detected by applying threshold area value to nuclei in the binary image. The splitting algorithm is applied to the overlapped nuclei. In first stage of splitting, circles are drawn on overlapped nuclei. The radius of the circles is calculated by using circle area formula, and each pixel's coordinates of overlapped nuclei are selected as center coordinates for each circle. The pixels in the circle that contains maximum number of intersected pixels in both the circle and the overlapped nuclei are removed from the overlapped nuclei, and the filled circle labeled as a nucleus. RESULTS The algorithm has been tested on histopathological images of healthy and damaged kidney tissues and compared with the results provided by an expert and three related studies. The results demonstrated that the proposed splitting algorithm can segment the overlapping nuclei with accuracy of 84%. CONCLUSIONS The study presents a novel algorithm splitting the overlapped nuclei in histopathological images and provides more accurate cell counting in histopathological analysis. Furthermore, the proposed splitting algorithm has the potential to be used in different fields to split any overlapped circular patterns.
Collapse
Affiliation(s)
- Faruk Serin
- Department of Computer Engineering, Faculty of Engineering, Munzur University, Tunceli, Turkey.
| | - Metin Erturkler
- Department of Computer Engineering, Faculty of Engineering, Inonu University, Malatya, Turkey
| | - Mehmet Gul
- Department of Embryology and Histology, Faculty of Medicine, Inonu University, Malatya, Turkey
| |
Collapse
|
46
|
Xu H, Lu C, Berendt R, Jha N, Mandal M. Automatic Nuclear Segmentation Using Multiscale Radial Line Scanning With Dynamic Programming. IEEE Trans Biomed Eng 2017; 64:2475-2485. [DOI: 10.1109/tbme.2017.2649485] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
47
|
Jørgensen AS, Rasmussen AM, Andersen NKM, Andersen SK, Emborg J, Røge R, Østergaard LR. Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides. Cytometry A 2017; 91:785-793. [DOI: 10.1002/cyto.a.23175] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 04/20/2017] [Accepted: 07/06/2017] [Indexed: 01/05/2023]
Affiliation(s)
| | | | | | - Simon Kragh Andersen
- Department of Health Science and Technology; Aalborg University; Aalborg Denmark
| | - Jonas Emborg
- Diagnostics & Genomics Group, Dako Denmark A/S; An Agilent Technologies Company; Glostrup Denmark
| | - Rasmus Røge
- Institute of Pathology, Aalborg University Hospital, Denmark and the Department of Clinical Medicine, Aalborg University; Aalborg Denmark
| | | |
Collapse
|
48
|
Ma Y, Jiang Z, Zhang H, Xie F, Zheng Y, Shi H, Zhao Y. Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation. IEEE J Biomed Health Inform 2017; 21:1114-1123. [DOI: 10.1109/jbhi.2016.2611615] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
49
|
Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A. A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1550-1560. [PMID: 28287963 DOI: 10.1109/tmi.2017.2677499] [Citation(s) in RCA: 325] [Impact Index Per Article: 46.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.
Collapse
|
50
|
Kumar R, Srivastava S, Srivastava R. A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:59-68. [PMID: 28688490 DOI: 10.1016/j.cmpb.2017.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 03/19/2017] [Accepted: 05/13/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. METHODS To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. RESULTS The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. CONCLUSIONS The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection.
Collapse
Affiliation(s)
- Rajesh Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India.
| | - Subodh Srivastava
- Department of Electronics and Communication Engineering, VNR VJIET, Hyderabad, India.
| | - Rajeev Srivastava
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India.
| |
Collapse
|