Accurate Cell Segmentation in Digital Pathology Images via Attention Enforced Networks

• URL: http://arxiv.org/abs/2012.07237v2
• Date: Sun, 27 Dec 2020 09:41:49 GMT
• Title: Accurate Cell Segmentation in Digital Pathology Images via Attention Enforced Networks
• Authors: Muyi Sun, Zeyi Yao, Guanhong Zhang
• Abstract summary: We propose an Attention Enforced Network (AENet) to integrate local features with global dependencies and weight effective channels adaptively. In the test stage, we present an individual color normalization method to deal with the stain variation problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic cell segmentation is an essential step in the pipeline of computer-aided diagnosis (CAD), such as the detection and grading of breast cancer. Accurate segmentation of cells can not only assist the pathologists to make a more precise diagnosis, but also save much time and labor. However, this task suffers from stain variation, cell inhomogeneous intensities, background clutters and cells from different tissues. To address these issues, we propose an Attention Enforced Network (AENet), which is built on spatial attention module and channel attention module, to integrate local features with global dependencies and weight effective channels adaptively. Besides, we introduce a feature fusion branch to bridge high-level and low-level features. Finally, the marker controlled watershed algorithm is applied to post-process the predicted segmentation maps for reducing the fragmented regions. In the test stage, we present an individual color normalization method to deal with the stain variation problem. We evaluate this model on the MoNuSeg dataset. The quantitative comparisons against several prior methods demonstrate the superiority of our approach.

Related papers

• GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation [24.85210810502592]
  This paper presents a modified version of MultiResU-Net for histopathology image segmentation. It is selected as the backbone for its ability to analyze and segment complex features at multiple scales. We validate our approach on two diverse breast cancer histopathology image datasets.
  arXiv  Detail & Related papers  (2024-06-12T19:17:17Z)
• AWGUNET: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images [26.333686941245197]
  We present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model.
  arXiv  Detail & Related papers  (2024-06-12T17:10:27Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy [0.20999222360659603]
  We developed a computational framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells.
  arXiv  Detail & Related papers  (2023-09-02T11:20:10Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• From WSI-level to Patch-level: Structure Prior Guided Binuclear Cell Fine-grained Detection [8.810499770542553]
  Binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. We propose a two-stage detection method inspired by the structure prior to BC based on deep learning. The coarse detection network is a multi-task detection framework based on circular bounding boxes for cells detection, and central key points for nucleus detection.
  arXiv  Detail & Related papers  (2022-08-26T12:32:05Z)
• W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image [5.065311617930521]
  We propose a W-shaped network for automatic nucleus detection. The first sub-task maps the original pathology image to the binary mask, then the binary mask is mapped to the density mask. After the task is split, the task's difficulty is significantly reduced, and the network's overall performance is improved.
  arXiv  Detail & Related papers  (2021-10-26T13:00:16Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.