An Automatic Learning-Based Framework for Robust Nucleus Segmentation

By Fuyong Xing, Yuanpu Xie, Lin F. Yang. IEEE Transactions on Medical Imaging, 2016

322 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d97e70d8fa6cbe4fcac9096491ce6e6a5e974e88

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Xing et al. [187] applied an optimized post-processing on the CNN probability map to detect individual nuclei.”
Breast cancer histopathological image classification using Convolutional Neural Networks (2016, background): “As a particular deep learning technique, Convolutional Neural Networks (CNNs) [13] have achieved success in image classification problems, including medical image analysis [20], [21], [22], [23].”
Deep neural network models for computational histopathology: A survey (2019, background): “Since training of CNN models is often complex and requires a larger training set, the earliest works (Wang et al., 2014; Kashif et al., 2016; Xing et al., 2016; Romo-Bucheli et al., 2016; Wang et al., 2016b) focused on integrating CNN with biologically interpretable handcrafted features.”
A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images (2018, methodology): “If the seed of a nucleus is generated, its contour could be obtained by using marker controlled watershed[9, 16] or region growing[17].”
DeepPap: Deep Convolutional Networks for Cervical Cell Classification (2017, unknown): “Additionally, ConvNets have demonstrated superior performance in the classiﬁcation of cell images, such as pleural cancer [41] and human epithelial-2 cell images [42].”
Digital Pathology and Artificial Intelligence (2019, background): “Similarly, [20] exploited the topology of the probability map using region-growing [21] to segment the individual nuclei.”
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map (2019, methodology): “In traditional image analysis, this problem can be addressed by imposing shape priors implicitly by the design of dedicated algorithms, such as deformable models [26], [31].”
A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images (2021, methodology): “DL is a ﬂexible and highly eﬃcient technique to solve the problems in medical imaging analysis, including lung cancer, breast cancer, and brain cancer detection [26] With DL techniques, automatic segmentation can be done effectively on a large amount of MRI datasets [27 .”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “[125] 30 brain tumor (BT), 51 pancreatic neuroendocrine tumor (NET), and 35 breast cancer (BC) images CNN precision/recall/F1 : 72%±20%/88%± 15%/77%±16% (BT), 84%±8%/93%± 5%/88%±4% (NET), 71%±14%/88%± 9%/78%±8% (BC) active contour Dice/HD/MAD: 85%±11%/5.”
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images (2018, methodology): “Precision and recall have been used in previous studies of nucleus segmentation in ﬂ uorescent images (47), and F 1 -score has also been adopted to measure segmentation performance in tissue samples (48).”
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images (2018, background): “neural networks (CNNs) have recently demonstrated stateof-the-art performance in nuclei segmentation [24, 30, 31].”