首页/论文/2003.03744

arXiv 2003.037442020-07-08cs.CV

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

Northeastern University（东北大学）；Chengdu University of Information Technology（成都信息工程大学）

Jinghua Zhang, Chen Li, Frank Kulwa, Xin Zhao, Changhao Sun, Zihan Li, Tao Jiang, Hong Li, Shouliang Qi

发表信息：: BioMed Research International, vol. 2020, Article ID 4621403, 27 pages, 2020
DOI：: 10.1155/2020/4621403

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, "mU-Net-B3", with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel "buffer" strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation

Northeastern University（东北大学）；Chengdu University of Information Technology（成都信息工程大学）；China Medical University（中国医科大学）；University of Luebeck（吕贝克大学）

The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2365 images and 13216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-RCNN, YOLOv3, YOLOv4, SSD and RetinaNet) and evaluation indices for testing and evalua…

2021-10-28cs.CV

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

Northeastern University（东北大学）；Chengdu University of Information Technology（成都信息工程大学）；University of Lübeck（吕贝克大学）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；Liaoning Shihua University（辽宁石油化工大学）；University of New South Wales（新南威尔士大学）

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related databases, not to mention the databases with GT images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT i…

2022-04-25cs.CV

A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

Northeastern University（东北大学）；Kindai University（近畿大学）；Jacobs University Bremen（不来梅雅各布大学）；China Medical University（中国医科大学）；Chengdu University of Information Technology（成都信息工程大学）

Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the …

2021-02-24cs.CV

A Comparison for Patch-level Classification of Deep Learning Methods on Transparent Environmental Microorganism Images: from Convolutional Neural Networks to Visual Transformers

Northeastern University（东北大学）；Chengdu University of Information Technology（成都信息工程大学）；University of Luebeck（吕贝克大学）

Nowadays, analysis of Transparent Environmental Microorganism Images (T-EM images) in the field of computer vision has gradually become a new and interesting spot. This paper compares different deep learning classification performance for the problem that T-EM images are challenging to analyze. We crop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the same proportion and then divide the two different pixel patches into foreground and background according to ground truth. We also use four convolutional neural networks and a novel ViT network model to compare the foreground and backg…

2021-07-21cs.CV

Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features

Northeastern University（东北大学）；University of Lübeck（吕贝克大学）；Kindai University（近畿大学）；Jacobs University Bremen（不来梅雅各布大学）

The use of Environmental Microorganisms (EMs) offers a highly efficient, low cost and harmless remedy to environmental pollution, by monitoring and decomposing of pollutants. This relies on how the EMs are correctly segmented and identified. With the aim of enhancing the segmentation of weakly visible EM images which are transparent, noisy and have low contrast, a Pairwise Deep Learning Feature Network (PDLF-Net) is proposed in this study. The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different b…

2022-08-31cs.CV

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

Microscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University（东北大学MBIE学院显微图像与医学图像分析组）；School of Control Engineering, Chengdu University of Information Technology（成都信息工程大学控制工程学院）；University of Lübeck（吕贝克大学）；China Medical University（中国医科大学）

In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyperparameter tuni…

2022-02-03cs.CV

更多相关论文收起更多论文

EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks

Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Huazhong University of Science and Technology（华中科技大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Chengdu University of Information Technology（成都信息工程大学）

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiven…

2021-02-20cs.CV

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

Northeastern University（东北大学）；Chengdu University of Information Technology（成都信息工程大学）；Stevens Institute of Technology（史蒂文斯理工学院）；National Chung Hsing University（中兴大学）；Huazhong University of Science and Technology（华中科技大学）；University of Luebeck（吕贝克大学）

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder--decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as cou…

2022-07-22cs.AIcs.CV

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Northeastern University（东北大学）；Hunan First Normal University（湖南第一师范学院）；University of Luebeck（吕贝克大学）

Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and time-consuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused …

2022-03-24cs.AIcs.CV

SemanticSugarBeets：用于检查甜菜收获与储存特性的多任务框架与数据集

SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets

AIT Austrian Institute of Technology Center for Vision, Automation & Control（AIT奥地利技术研究所视觉、自动化与控制中心）

提出甜菜收获与储存特性检查的多任务框架和高质量数据集，实现检测、语义分割与质量估计，实验取得mAP50-95 98.8和mIoU 64.0的优异性能。

2025-04-23cs.CVcs.LG

Continuous Conditional Random Field Convolution for Point Cloud Segmentation

School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Alliance Sorbonne Université, Université de technologie de Compiègne, CNRS（索邦大学联盟、贡比涅技术大学、法国国家科学研究中心）

Point cloud segmentation is the foundation of 3D environmental perception for modern intelligent systems. To solve this problem and image segmentation, conditional random fields (CRFs) are usually formulated as discrete models in label space to encourage label consistency, which is actually a kind of postprocessing. In this paper, we reconsider the CRF in feature space for point cloud segmentation because it can capture the structure of features well to improve the representation ability of features rather than simply smoothing. Therefore, we first model the point cloud features with a continu…

2021-10-12cs.CVcs.RO

Efficient Unsupervised Learning for Plankton Images

University of Genova（热那亚大学）；INFN（意大利国家核物理研究所）；Istituto Italiano di Tecnologia（意大利理工学院）；MIT（麻省理工学院）

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. The adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired da…

2022-09-14cs.AIcs.CVcs.LG