ArticleSensors (Basel, Switzerland)2022
Convolutional Blur Attention Network for Cell Nuclei Segmentation.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Benign Share Benefit to Malignant: Balanced Mixing on Feature Space for Imbalanced Breast Cancer Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Hardware Implementation of Improved Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping Version 2.Sensors (Basel, Switzerland) · 2025Article
- An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.Sensors (Basel, Switzerland) · 2024Article
- Complex-Phase Steel Microstructure Segmentation Using UNet: Analysis across Different Magnifications and Steel Types.Materials (Basel, Switzerland) · 2023Article
- Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging.Frontiers in genetics · 2022Article
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4 authors.
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Abstract
Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditions make nucleus segmentation a challenging task. In this work, we present a new deep learning-based method for cell nucleus segmentation. The proposed convolutional blur attention (CBA) network consists of downsampling and upsampling procedures. A blur attention module and a blur pooling operation are used to retain the feature salience and avoid noise generation in the downsampling procedure. A pyramid blur pooling (PBP) module is proposed to capture the multi-scale information in the upsampling procedure. The superiority of the proposed method has been compared with a few prior segmentation models, namely U-Net, ENet, SegNet, LinkNet, and Mask RCNN on the 2018 Data Science Bowl (DSB) challenge dataset and the multi-organ nucleus segmentation (MoNuSeg) at MICCAI 2018. The Dice similarity coefficient and some evaluation matrices, such as F1 score, recall, precision, and average Jaccard index (
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