A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation.
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 Con...
| Publicado en: | BioMed Research International pp. 1 - 28 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/8/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144459938&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144459938 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/8/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 144459938 144459938 144459938 10.1155/2020/4621403 144459938 ppf: 1 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation. aug: au: Zhang, Jinghua Li, Chen Kulwa, Frank Zhao, Xin Sun, Changhao Li, Zihan Jiang, Tao Li, Hong Qi, Shouliang affil: Microscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang 110169, China sug: subj: Neural Networks (Computer) Image Enhancement Methods Environmental Microbiology Evaluation Environmental Pollution Analysis Human Conceptual Framework Comparative Studies Validity ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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