A Classification Scheme for Lymphocyte Segmentation in H&amp;E Stained Histology Images

Kuse, Manohar; Sharma, Tanuj; Gupta, Sumit

doi:10.1007/978-3-642-17711-8_24

Cited by 33 publications

(25 citation statements)

References 11 publications

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“…Several methods have been proposed for automatic Gleason grading [7][8][9][10][11][12][13][14][15][16]. We categorize those papers based on the features they use and review them in the following.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Structural techniques are based on cell segmentation and then feature extraction [7][8][9][10][11]. Farjam et al [7] extracted structural features of the glands and used them in a tree-structured algorithm to classify the images into five Gleason grades of 1-5.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A microscopic image segmentation method was proposed in [11]. The authors transformed the images to the HSV domain and used mean shift clustering for segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This motivated us to base our morphological feature extraction algorithm on cell nuclei detection. To achieve this, we propose using the mean shift clustering algorithm [34] for the task of color approximation and then thresholding in HSV color space to separate cell nuclei from other parts of the tissue, similar to [11]. The mean shift algorithm uses a window around each data point and calculates the mean.…”

Section: Morphological Featuresmentioning

confidence: 99%

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Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning

Rezaeilouyeh

Mahoor

2016

J. Imaging

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The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine systems and can analyze signals at various orientations and scales and detect singularities, such as image edges. These properties make the Shearlet transform more suitable for Gleason grading compared to the other transform-based feature extraction methods, such as Fourier transform, wavelet transform, etc. We also extract color channel histograms and morphological features. These features are the essential building blocks of what pathologists consider when they perform Gleason grading. Then, we use the multiple kernel learning (MKL) algorithm for fusing all three different types of extracted features. We use support vector machines (SVM) equipped with MKL for the classification of prostate slides with different Gleason grades. Using the proposed method, we achieved high classification accuracy in a dataset containing 100 prostate cancer sample images of Gleason Grades 2-5.Gland units contain epithelial cells around the lumen. When cancer happens, epithelial cells randomly duplicate, disturbing the normal structure of glands, as shown in Figure 2b,c.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…A microscopic image segmentation method was proposed in [11]. The authors transformed the images to the HSV domain and used mean shift clustering for segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Morphological Featuresmentioning

confidence: 99%

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Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning

Rezaeilouyeh

Mahoor

2016

J. Imaging

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“…Some of them use blue channel only as it gives greatest contrast between brown and blue but loose information about brown colour spread in G an R channels [31], the other propose combination of all channels of RGB as: -“brown axis” = B-0.3*(R+G) Tadrous 2010, [32], -colour deconvolution in which three well defined colour vectors, describing new colours in old colour space, should be achieved as calibration information (Ruifrok and Johanston 2001) [33-35], -“de-staining” algorithm separating up to three visually distinct colours to effect selective contrast [32]. Minority of algorithms uses HSV colour model in which detection of the brown colour can be simply rotation of the hue axes by Kuse [36]. All of threshold methods suffer from a lack of universality as they are adjusted by specifics image parameters: level of contrast [37-39] or degree of saturation [8] and so on.…”

Section: Introductionmentioning

confidence: 99%

Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3’-Diaminobenzidine&Haematoxylin

et al. 2013

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The comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the ’brown component’ extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without undercutting the area for true positive objects but with extra false positive objects. The Sauvola and the Bernsen methods gives complementary results what will be exploited when the new method of virtual tissue slides segmentation be develop.Virtual SlidesThe virtual slides for this article can be found here: slide 1: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617947952577 and slide 2: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617948230017.

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Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach

Lahiani

Gildenblat²,

Albarqouni

et al. 2019

Lecture Notes in Computer Science

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Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stains on the same tissue segment. In this paper, we present a sample application that generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.

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A Classification Scheme for Lymphocyte Segmentation in H&E Stained Histology Images

Cited by 33 publications

References 11 publications

Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning

Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning

Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3’-Diaminobenzidine&Haematoxylin

Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach

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