Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information
In collaboration with the Institute of Virology, Philipps University, Marburg, a deep-learning-based method that recognizes and classifies cell organelles based on the distribution of subviral particles in fluorescence microscopy images of virus-infected cells has been further developed. In this wor...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2021-10-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2021-2047 |
| _version_ | 1811246823865581568 |
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| author | Busch Nils Rausch Andreas Schanze Thomas |
| author_facet | Busch Nils Rausch Andreas Schanze Thomas |
| author_sort | Busch Nils |
| collection | DOAJ |
| description | In collaboration with the Institute of Virology, Philipps University, Marburg, a deep-learning-based method that recognizes and classifies cell organelles based on the distribution of subviral particles in fluorescence microscopy images of virus-infected cells has been further developed. In this work a method to recognize cell organelles by means of partial image information is extended. The focus is on investigating loss of accuracy by only providing information about subviral particles and not all cell organelles to an adopted Mask-R convolutional neural network. Our results show that the subviral particle distribution holds information about the cell morphology, thus making it possible to use it for cell organelle-labelling. |
| first_indexed | 2024-04-12T14:58:54Z |
| format | Article |
| id | doaj.art-7bd7a93e362f4e6fb90f0ebb02971317 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-04-12T14:58:54Z |
| publishDate | 2021-10-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-7bd7a93e362f4e6fb90f0ebb029713172022-12-22T03:28:07ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042021-10-017218318610.1515/cdbme-2021-2047Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution informationBusch Nils0Rausch Andreas1Schanze Thomas2Institute for Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) - University of Applied Sciences,Gießen, GermanyInstitute for Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) - University of Applied Sciences,Gießen, GermanyInstitute for Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) - University of Applied Sciences,Gießen, GermanyIn collaboration with the Institute of Virology, Philipps University, Marburg, a deep-learning-based method that recognizes and classifies cell organelles based on the distribution of subviral particles in fluorescence microscopy images of virus-infected cells has been further developed. In this work a method to recognize cell organelles by means of partial image information is extended. The focus is on investigating loss of accuracy by only providing information about subviral particles and not all cell organelles to an adopted Mask-R convolutional neural network. Our results show that the subviral particle distribution holds information about the cell morphology, thus making it possible to use it for cell organelle-labelling.https://doi.org/10.1515/cdbme-2021-2047deep-learningobject detectionsubviral particles. |
| spellingShingle | Busch Nils Rausch Andreas Schanze Thomas Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information Current Directions in Biomedical Engineering deep-learning object detection subviral particles. |
| title | Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information |
| title_full | Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information |
| title_fullStr | Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information |
| title_full_unstemmed | Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information |
| title_short | Estimation of accuracy loss by training a deep-learning-based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information |
| title_sort | estimation of accuracy loss by training a deep learning based cell organelle recognition software using full dataset and a reduced dataset containing only subviral particle distribution information |
| topic | deep-learning object detection subviral particles. |
| url | https://doi.org/10.1515/cdbme-2021-2047 |
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