Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks

Pan, Xipeng; Li, Lingqiao; Yang, Huihua; Liu, Zhenbing; Yang, Jinxin; Zhao, Lingling; Fan, Yongxian

doi:10.1016/j.neucom.2016.08.103

Cited by 110 publications

(45 citation statements)

References 31 publications

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“…This type of approach has also been used to detect cell locations in different types of microscopic images such as live cell [27], fluorescent [28], and zebrafish [29] images. As an alternative, nuclei are located by postprocessing the class labels with techniques such as morphological operations [30] and region growing [31]. In [32], after obtaining a nucleus label map, nuclei's bounding boxes are estimated by training another deep neural network.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images

Sarı

Gündüz-Demir

2019

IEEE Trans. Med. Imaging

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Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly rely on features that they use, and thus, their success strictly depends on the ability of these features successfully quantifying the histopathology domain. With this motivation, this paper presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This feature extractor has three main contributions: First, it proposes to identify salient subregions in an image, based on domain-specific prior knowledge, and to quantify the image by employing only the characteristics of these subregions instead of considering the characteristics of all image locations. Second, it introduces a new deep learning based technique that quantizes the salient subregions by extracting a set of features directly learned on image data and uses the distribution of these quantizations for image representation and classification. To this end, the proposed deep learning based technique constructs a deep belief network of restricted Boltzmann machines (RBMs), defines the activation values of the hidden unit nodes in the final RBM as the features, and learns the quantizations by clustering these features in an unsupervised way. Third, this extractor is the first example of successfully using restricted Boltzmann machines in the domain of histopathological image analysis. Our experiments on microscopic colon tissue images reveal that the proposed feature extractor is effective to obtain more accurate classification results compared to its counterparts.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images

Sarı

Gündüz-Demir

2019

IEEE Trans. Med. Imaging

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“…Finally, segmentation results are refined by morphological operators such as a marker-based watershed. In [22], Nuclear segmentation has been performed by converting the RGB image into gray scale, denoising the image, and applying the CNN to separate background and foreground. Finally, nuclear segmentation is refined by morphological operators.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Deep Learning Models Differentiate Tumor Grades from H&E Stained Histology Sections

Khoshdeli

Borowsky

Parvin

2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Nuclear segmentation is an important step for profiling aberrant regions of histology sections. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy). The problem is further complicated as a result of variations in sample preparation. It is shown and validated that fusion of very deep convolutional networks overcomes (i) complexities associated with multiple nuclear phenotypes, and (ii) separation of overlapping nuclei. The fusion relies on integrating of networks that learn region-and boundary-based representations. The system has been validated on a diverse set of nuclear phenotypes that correspond to the breast and brain histology sections.

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“…Infrared images distinguish targets from background based on differences in thermal radiation. By combining the complementary information of visible and infrared image, it is possible to generate fused images that are more conducive to human decision-making or computer vision tasks, which has been applied to many fields such as the military, target detection, surveillance and so on [4][5][6][7][8][9]. An excellent image fusion algorithm must contain the following conditions.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

et al. 2020

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Infrared and visible image fusion technology provides many benefits for human vision and computer image processing tasks, including enriched useful information and enhanced surveillance capabilities. However, existing fusion algorithms have faced a great challenge to effectively integrate visual features from complex source images. In this paper, we design a novel infrared and visible image fusion algorithm based on visual attention technology, in which a special visual attention system and a feature fusion strategy based on the saliency maps are proposed. Special visual attention system first utilizes the co-occurrence matrix to calculate the image texture complication, which can select a particular modality to compute a saliency map. Moreover, we improved the iterative operator of the original visual attention model (VAM), a fair competition mechanism is designed to ensure that the visual feature in detail regions can be extracted accurately. For the feature fusion strategy, we use the obtained saliency map to combine the visual attention features, and appropriately enhance the tiny features to ensure that the weak targets can be observed. Different from the general fusion algorithm, the proposed algorithm not only preserve the interesting region but also contain rich tiny details, which can improve the visual ability of human and computer. Moreover, experimental results in complicated ambient conditions show that the proposed algorithm in this paper outperforms state-of-the-art algorithms in both qualitative and quantitative evaluations, and this study can extend to the field of other-type image fusion.

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Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks

Cited by 110 publications

References 31 publications

Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images

Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images

Deep Learning Models Differentiate Tumor Grades from H&E Stained Histology Sections

An Efficient Method for Infrared and Visual Images Fusion Based on Visual Attention Technique

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