Modern convolutional object detectors for nuclei detection on pleural effusion cytology images

Baykal, Elif; Doğan, Hülya; Erçin, Mustafa Emre; Ersöz, Şafak; Ekinci, Murat

doi:10.1007/s11042-019-7461-3

Cited by 9 publications

(4 citation statements)

References 31 publications

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“…To date, the topic of hemosiderophage classification and quantification has not been approached using computer vision methods. However, there have been numerous studies in the past decades with the goal of detecting cells, nuclei and mitotic figures for multiple modalities like digital fluorescence microscopy and histopathology [17][18][19] . Historically, this started as hand-crafted low-level feature extraction [20][21][22] .…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

Marzahl

Aubreville

Bertram

et al. 2020

Sci Rep

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Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter-and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss' kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter-and intrarater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI. Patients with pulmonary hemorrhage (P-Hem) suffer from repeated bleeding into the lungs, which can result in dyspnea and if untreated, may have life threatening consequences 1. There are various causes which lead to P-Hem, including drug abuse, premature birth, leukaemia, autoimmune disorders and immunodeficiencies 2-6. In this paper, we focus on a special subtype of P-Hem called exercise-induced pulmonary hemorrhage (EIPH) in horses. Although EIPH also affects healthy human athletes 7 and racing greyhounds 8 , it is diagnosed most commonly in racing horses and causes reduced athletic performance 9-12. The gold standard for diagnosis of P-Hem in humans and equine animals is to perform cytology of bronchoalveolar lavage fluid (BALF) 4,13 using a scoring system as explained by Golde et al. 4. The red blood cells of the bleeding are degraded into an iron-storage complex called hemosiderin by alveolar macrophages. Hemosiderin-laden macrophages are called hemosiderophages. Prior to microscopic evaluation, the cells are extracted by the BALF procedure and stained with Perlss' Prussian Blue 14 or Turnbull's Blue 15 in order to visualise the iron pigments contained in the hemosiderin. According to the commonly used scoring system (macrophages hemosiderin score) by Golde et al. 4 , alveolar macrophages can be distinguished into five grades depending on their hemosiderin content. This scoring system is based on the principle that a higher score correlates with increased alveolar bleeding 16 .

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

Marzahl

Aubreville

Bertram

et al. 2020

Sci Rep

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“…Based on ResNet, the best results of the ImageNet Large Scale Visual Recognition Challenge 2015 (ILSVRC 2015) and the breakthrough for improving its performance in many fields were achieved; these included image recognition, image detection, and image localization. ResNet has been widely applied in the field of biomedical imaging, having been used for cell classification [28,29], cell detection [30,31], early cancer detection [32,33], etc.…”

Section: Resnetmentioning

confidence: 99%

An Imbalanced Image Classification Method for the Cell Cycle Phase

2021

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The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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“…Nucleus Detection There have been a surge of interest in the application of deep learning to nucleus detection [25,28,8,18,20]. Some works adapts a topdown object detector to histopathology images [3,2], while others employ a PMap representation where values represent the proximity to or probability of a nucleus center [26,21,6]. The prediction of PMap can be formed as either a classification or regression problem.…”

Section: Related Workmentioning

confidence: 99%

A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation

Zhang¹,

Kortylewski²,

Liu³

et al. 2021

Preprint

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The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These deep neural networks usually require large numbers of annotated data to train vast parameters. However, it takes significant effort to annotate a large histopathology dataset. We propose to build a data-efficient model, which only requires partial annotation, specifically on isolated nucleus, rather than on the whole slide image. It exploits shallow features as its backbone and is light-weight, therefore a small number of data is sufficient for training. What's more, it is a generative compositional model, which enjoys interpretability in its prediction. The proposed method could be an alternative solution for the data-hungry problem of deep learning methods.

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Modern convolutional object detectors for nuclei detection on pleural effusion cytology images

Cited by 9 publications

References 31 publications

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

An Imbalanced Image Classification Method for the Cell Cycle Phase

A Light-weight Interpretable CompositionalNetwork for Nuclei Detection and Weakly-supervised Segmentation

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