MASG-GAN: A multi-view attention superpixel-guided generative adversarial network for efficient and simultaneous histopathology image segmentation and classification

Zhang, Huaqi; Liu, Jie; Yu, Zekuan; Wang, Pengyu

doi:10.1016/j.neucom.2021.08.039

Cited by 21 publications

(6 citation statements)

References 29 publications

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“…It could achieve a DICE metric of 0.94 on the BraTS-2015 and BraTS-2017 datasets, while no other method at the time exceeded 0.9. Zhang et al [39] proposed a Multi-view Attention Superpixel-guided GAN to implement cell nucleus segmentation and tissue classification, which also achieved the effect of multi-task learning. The network uses a superpixel segmentation method based on the Bounded Asymmetric Gaussian Mixture Model and a Multiview Attention Module to improve the precision of segmentation of cell nuclei with small areas and fuzzy boundaries.…”

Section: Related Workmentioning

confidence: 99%

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

He,

Zhu,

et al. 2023

IET Image Processing

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Artificial intelligence decision systems play an important supporting role in the field of medical information. Medical image analysis is an important part of decision systems and an even more important part of medical diagnosis and treatment. The wealth of cellular information in histopathological images makes them a reliable means of diagnosing tumors. However, due to the large size, high resolution, and complex background structure of pathology images, deep learning methods still have various difficulties in the recognition of pathology images. Based on this, this study proposes a two‐stage continuous improvement‐based approach for pathology image recognition in medical decision systems. For pathology images with complex backgrounds, normalization and enhancement is performed to remove the effects of noise color, and light‐dark inconsistencies on the segmentation network. The continuous refinement PSP Net (CRPSPNet) is then designed for accurate recognition of the pathology images. CRPSPNet is divided into two stages: Pyramid Scene Parsing Network segmentation to obtain coarse segmentation results; and continuous refinement model refines the results of the first stage. Experiments using more than 1,000 osteosarcoma pathology images have shown that the method gives more accurate results with fewer computer resources and processing time than traditional optimization models. Its Intersection over Union achieves 0.76.

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Section: Related Workmentioning

confidence: 99%

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

He,

Zhu,

et al. 2023

IET Image Processing

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“…On the other hand, the tissue segmentation task takes the histopathology image as input and segments the tissues that are composed of a group of cells in the input image with certain characteristics and structures (i.e., gland, tumor-infiltrating lymphocytes, etc.). These quantitatively measured tissues are also a crucial indicator for the diagnosis and prognosis of human cancers [ 49 , 50 ].…”

Section: Pathology Image Segmentationmentioning

confidence: 99%

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Cheng

Huang

et al. 2022

Cancers

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With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

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“…Zhang et al, for instance, used superpixel clustering to compute a probability map for nuclei presegmentation, which was used as auxiliary input to the subsequent tissue classification network. 25 Nguyen et al directly segmented breast tissue samples into coherent tissue regions using a graph-based superpixel algorithm. 26 The authors, however, merely performed a segmentation and did not infer labels for the computed superpixels.…”

Section: Superpixel Classificationmentioning

confidence: 99%

Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification

et al. 2022

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Purpose: Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSIs), however, poses a challenge in terms of computation time. In this regard, the analysis of nonoverlapping patches outperforms pixelwise segmentation approaches but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies. Approach:We propose to subdivide the WSI into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels. Afterward, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label. We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis. Results:The algorithm has been developed on 159 hand-annotated WSIs of colon resections and its performance is compared with an analysis without prior segmentation. The algorithm shows an average speed-up of 41% and an increase in accuracy from 93.8% to 95.7%. By assigning a rejection label to uncertain superpixels, we further increase the accuracy by 0.4%. While tumor area estimation shows high concordance to the annotated area, the analysis of tumor composition highlights limitations of our approach. Conclusion:By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints.

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MASG-GAN: A multi-view attention superpixel-guided generative adversarial network for efficient and simultaneous histopathology image segmentation and classification

Cited by 21 publications

References 29 publications

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification

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