Simultaneous Cell Detection and Classification in Bone Marrow Histology Images

Song, Tzu-Hsi; Sánchez, Vı́ctor; EIDaly, Hesham; Rajpoot, Nasir M.

doi:10.1109/jbhi.2018.2878945

Cited by 46 publications

(24 citation statements)

References 25 publications

(45 reference statements)

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“…The results show that most of the detection networks are capable of performing the requested task: finding hematopoietic cells in whole slide microscopy images of human bone marrow. Subsequent (or parallel) classification tasks will benefit from the high resolution, enabling a more detailed analysis compared to the previous state-of-the-art methods[ 2 3 ] on human bone marrow images. While several methods exist for the analysis of peripheral blood images, these do not cover the entire hematopoiesis, which is important for the diagnosis of several hematopoietic diseases.…”

Section: Discussionmentioning

confidence: 99%

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State of the Art Cell Detection in Bone Marrow Whole Slide Images

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Özkan

Crysandt

et al. 2021

Journal of Pathology Informatics

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Context: Diseases of the hematopoietic system such as leukemia is diagnosed using bone marrow samples. The cell type distribution plays a major role but requires manual analysis of different cell types in microscopy images. Aims: Automated analysis of bone marrow samples requires detection and classification of different cell types. In this work, we propose and compare algorithms for cell localization, which is a key component in automated bone marrow analysis. Settings and Design: We research fully supervised detection architectures but also propose and evaluate several techniques utilizing weak annotations in a segmentation network. We further incorporate typical cell-like artifacts into our analysis. Whole slide microscopy images are acquired from the human bone marrow samples and annotated by expert hematologists. Subjects and Methods: We adapt and evaluate state-of-the-art detection networks. We further propose to utilize the popular U-Net for cell detection by applying suitable preprocessing steps to the annotations. Statistical Analysis Used: Evaluations are performed on a held-out dataset using multiple metrics based on the two different matching algorithms. Results: The results show that the detection of cells in hematopoietic images using state-of-the-art detection networks yields very accurate results. U-Net-based methods are able to slightly improve detection results using adequate preprocessing – despite artifacts and weak annotations. Conclusions: In this work, we propose, U-Net-based cell detection methods and compare with state-of-the-art detection methods for the localization of hematopoietic cells in high-resolution bone marrow images. We show that even with weak annotations and cell-like artifacts, cells can be localized with high precision.

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Section: Discussionmentioning

confidence: 99%

“…In 2017 and 2018, Song et al . [ 2 3 ] performed research related to the detection of two groups of cell types in bone marrow images. While they show promising results, the data only has ×40 magnification and only allows distinguishing between two groups of cell types.…”

Section: Introductionmentioning

confidence: 99%

State of the Art Cell Detection in Bone Marrow Whole Slide Images

Gräbel

Özkan

Crysandt

et al. 2021

Journal of Pathology Informatics

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“…Regarding the application of GANs to pathological images, Song et al proposed a method to generate histopathological images of bone marrow cells to improve the performance of automated bone marrow cell classification [21]. However, applications to cytology and image generation (in which multiple cells exist) have not been successful.…”

Section: Plos Onementioning

confidence: 99%

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

et al. 2020

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Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant features and achieved an accuracy of 81.0%. To further improve the DCNN's performance, it is necessary to train the network using more images. However, it is difficult to acquire cell images which contain a various cytological features with the use of many manual operations with a microscope. Therefore, in this study, we aim to improve the classification accuracy of a DCNN with the use of actual and synthesized cytological images with a generative adversarial network (GAN). Based on the proposed method, patch images were obtained from a microscopy image. Accordingly, these generated many additional similar images using a GAN. In this study, we introduce progressive growing of GANs (PGGAN), which enables the generation of high-resolution images. The use of these images allowed us to pretrain a DCNN. The DCNN was then fine-tuned using actual patch images. To confirm the effectiveness of the proposed method, we first evaluated the quality of the images which were generated by PGGAN and by a conventional deep convolutional GAN. We then evaluated the classification performance of benign and malignant cells, and confirmed that the generated images had characteristics similar to those of the actual images. Accordingly, we determined that the overall classification accuracy of lung cells was 85.3% which was improved by approximately 4.3% compared to a previously conducted study without pretraining using GAN-generated images. Based on these results, we confirmed that our proposed method will be effective for the classification of cytological images in cases at which only limited data are acquired.

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“…Specifically, two methods for automatic cell segmentation use autoencoders for unsupervised cell detection in histopathological slides: Hou et al [51] proposed a general-purpose method for nuclei segmentation, whereas Song et al [52] designed a model to segment erythroid and myeloid cells in bone marrow. These methods are designed to detect individual cells or slightly overlapping cells in cases where the maximum possible size of a cell is precisely determined.…”

Section: Related Workmentioning

confidence: 99%

Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders

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Jaworek-Korjakowska

et al. 2020

Sensors

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In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.

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Simultaneous Cell Detection and Classification in Bone Marrow Histology Images

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 46 publications

References 25 publications

State of the Art Cell Detection in Bone Marrow Whole Slide Images

State of the Art Cell Detection in Bone Marrow Whole Slide Images

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders

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