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A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation. | LitMetric

A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation.

Biomed Res Int

Microscopic Image and Medical Image Analysis Group, MBIE College, Northeastern University, Shenyang 110169, China.

Published: April 2021

AI Article Synopsis

Article Abstract

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a (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 (CNN), namely, "mU-Net-B3", with a dense (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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366198PMC
http://dx.doi.org/10.1155/2020/4621403DOI Listing

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