Lymphocyte Segmentation Using the Transferable Belief Model

Introduction:The White Blood Cell (WBC) differential count yields clinically relevant information about health and disease. Currently, pathologists manually annotate the WBCs, which is time consuming and susceptible to error, due to the tedious nature of the process. This study aims at automation of the Differential Blood Count (DBC) process, so as to increase productivity and eliminate human errors.Materials and Methods:The proposed system takes the peripheral Leishman blood stain images as the input and generates a count for each of the WBC subtypes. The digitized microscopic images are stain normalized for the segmentation, to be consistent over a diverse set of slide images. Active contours are employed for robust segmentation of the WBC nucleus and cytoplasm. The seed points are generated by processing the images in Hue-Saturation-Value (HSV) color space. An efficient method for computing a new feature, ‘number of lobes,’ for discrimination of WBC subtypes, is introduced in this article. This method is based on the concept of minimization of the compactness of each lobe. The Naive Bayes classifier, with Laplacian correction, provides a fast, efficient, and robust solution to multiclass categorization problems. This classifier is characterized by incremental learning and can also be embedded within the database systems.Results:An overall accuracy of 92.45% and 92.72% over the training and testing sets has been obtained, respectively.Conclusion:Thus, incremental learning is inducted into the Naive Bayes Classifier, to facilitate fast, robust, and efficient classification, which is evident from the high sensitivity achieved for all the subtypes of WBCs.

Section: Methodsmentioning

confidence: 99%

Scalable system for classification of white blood cells from Leishman stained blood stain images

Mathur

Tripathi

Kuse

2013

“…[ 31 ] In another study, a method based on a transferable belief model obtained 94% sensitivity and 81% precision in lymphocyte detection on cell level when evaluated with 10 images (400 × 400 pixels). [ 32 ] Evaluation done with only a few images or cells might not reflect the true performance, making the comparison to other studies difficult.…”

Section: Discussionmentioning

confidence: 99%

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

Turkki

Linder

Kovanen

et al. 2016

Background:Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples.Methods:Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists.Results:In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92–0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87–0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78).Conclusions:Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists’ visual assessment.

“…We employed the standard Hematoxylin stain vector, as suggested in,[ 15 ] in order to identify pixels containing the candidate nuclei. The phase symmetry measure was then used to identify lymphocytes, without using any textural measures or any kind of supervised or unsupervised classifier, as was the case with some of the competing algorithms in that contest[ 22 – 25 ] Table 3 shows the results of our algorithm for lymphocyte detection as compared to four other competing methods. We quote the figures for last four of the seven aforementioned performance measures from.…”

Section: Resultsmentioning

confidence: 99%

Local isotropic phase symmetry measure for detection of beta cells and lymphocytes

Kuse

Wang

Kalasannavar

et al. 2012

Diabetes can be associated with a reduction in functional β cell mass, which must be restored if the disease is to be cured or progress is to be arrested. To study the cell count, it is also necessary to determine the number of nuclei within the insulin stained area. It can take a single experimentalist several months to complete a single study of this kind, results of which may still be quite subjective. In this paper, we propose a framework based on a novel measure of local symmetry for detection of cells. The local isotropic phase symmetry measure (LIPSyM) is designed to give high values at or near the cell centers. We demonstrate the effectiveness of our algorithm for detection of two types of specific cells in histology images, cells in mouse pancreatic sections and lymphocytes in human breast tissue. Experimental results for these two problems show that our algorithm performs better than human experts for the former problem, and outperforms the best reported results for the latter.