LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks

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 .

Reconstruction enhanced probabilistic model for semisupervised tongue image segmentation

“…With the emerging technique development of deep learning 17 such as convolution neural networks (CNN), there are significant performance improvements for different tasks in the domain of computer vision, for example, image classification, 18,19 object detection, 20,21 and image segmentation. [22][23][24] Different from traditional machine learning methods [25][26][27][28] applied in different domains, [29][30][31]…”

Tonguenet: Accurate Localization and Segmentation for Tongue Images Using Deep Neural Networks

“…Benefiting from the development of deep learning based techniques, such as deep convolution neural networks, significant performance improvements have been witnessed in the field of computer vision; e.g. object detection [18], image classification [19], and image segmentation [20], [21]. However, few works employ deep learning-based methods for tongue segmentation due to the difficulty of collecting and labeling the tongue images.…”

Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features

“…In literature, the maximum techniques for blood cell localization [22,23], segmentation and classification are based on thresholding [24][25][26][27][28][29][30][31][32][33][34][35][36]. Thresholding methods cannot provide precise results if cells on the blood smear image are occluded with other cells.…”