Transfer Shape Modeling Towards High-Throughput Microscopy Image Segmentation

Xing, Fuyong; Shi, Xiaoshuang; Zhang, Zizhao; Cai, Jinzheng; Xie, Yuanpu; Yang, Lin

doi:10.1007/978-3-319-46726-9_22

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Despite a large body of research work on image classification and segmentation, the process of extracting, mining, and interpreting information from digital slide images remains a difficult task (Xie et al, 2016; Xing et al, 2016; Chennubhotla et al, 2017; Senaras and Gurcan, 2018). There are a number of challenges that segmentation and classification algorithms have to address.…”

Section: Introductionmentioning

confidence: 99%

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Graham

Kurç

et al. 2019

Front. Bioeng. Biotechnol.

239

137

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High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge.

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Section: Introductionmentioning

confidence: 99%

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Graham

Kurç

et al. 2019

Front. Bioeng. Biotechnol.

239

137

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“…In addition, instead of conventional RNNs, advanced memory networks [199,66] could be useful to handle contour reasoning. In addition, using shape priors [219] is popular to main the structure of objects in medical image segmentation because organs or cells usually have similar shapes. [26,51] use deep Boltzmann machines to model hierarchical structures to constrain the evolution of the shape-driven variational models.…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

Zhang¹,

Xing²,

Su³

et al. 2017

Preprint

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The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis. Deep convolution neural networks (CNNs), as one of the most important branch of the deep learning family, have been widely investigated for various computer-aided diagnosis tasks including long-term problems and continuously emerging new problems. Image contour detection is a fundamental but challenging task that has been studied for more than four decades. Recently, we have witnessed the significantly improved performance of contour detection thanks to the development of CNNs. Beyond purusing performance in existing natural image benchmarks, contour detection plays a particularly important role in medical image analysis. Segmenting various objects from radiology images or pathology images requires accurate detection of contours. However, some problems, such as discontinuity and shape constraints, are insufficiently studied in CNNs. It is necessary to clarify the challenges to encourage further exploration. The performance of CNN based contour detection relies on the state-of-the-art CNN architectures. Careful investigation of their design principles and motivations is critical and beneficial to contour detection. In this paper, we first review recent development of medical image contour detection and point out the current confronting challenges and problems. We discuss the development of general CNNs and their applications in image contours (or edges) detection. We compare those methods in detail, clarify their strengthens and weaknesses. Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis. We expect the paper to cover comprehensive technical ingredients of advanced CNNs to enrich the study in the medical image domain.

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“…In the test phase, the outputs of CNNs that analyze rotated and mirrored images are pooled to build final probability maps. In addition, kernel smoothing minimizes extra noise, allowing for faster detection of mitotic centroids through local maxima (with non-maximum suppression), using the same previous method to predict the nuclei for pancreatic neuroendocrine, brain, and breast cancer by CNNs [26][27][28], respectively. Phase-contrast microscopy images also predict circulating tumor cells in the blood [29].…”

Section: Cells Detectionmentioning

confidence: 99%

Residual Network with Attention to Neural Cells Segmentation

Abbas

Abdulmunim

2023

Iraqi Journal of Science

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Many neuroscience applications, including understanding the evolution of the brain, rely on neural cell instance segmentation, which seeks to integrate the identification and segmentation of neuronal cells in microscopic imagery. However, the task is complicated by cell adhesion, deformation, vague cell outlines, low-contrast cell protrusion structures, and background imperfections. On the other hand, existing segmentation approaches frequently produce inaccurate findings. As a result, an effective strategy for using the residual network with attention to segment cells is suggested in this paper. The segmentation mask of neural cells may be accurately predicted. This method is built on U-net, with EfficientNet serving as the encoder's backbone. The attention approach is employed in the detection and segmentation modules to guide the model's attention to the most valuable features. A massive collection of neural cell microscopic images tests the proposed method. According to the findings of the experiments, this technology can accurately detect and segment neuronal cell occurrences with an intersection over the union IoU of 95.47 and a Dice-Coeff of 98.34, which is superior to current state-of-the-art approaches.

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Transfer Shape Modeling Towards High-Throughput Microscopy Image Segmentation

Cited by 9 publications

References 18 publications

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Methods for Segmentation and Classification of Digital Microscopy Tissue Images

Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

Residual Network with Attention to Neural Cells Segmentation

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