Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences
Pose estimation of C. elegans in image sequences is challenging and even more difficult in low-resolution images. Problems range from occlusions, loss of worm identity, and overlaps to aggregations that are too complex or difficult to resolve, even for the human eye. Neural networks, on the other ha...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023019229 |
| _version_ | 1827957501223501824 |
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| author | Pablo E. Layana Castro Antonio García Garví Antonio-José Sánchez-Salmerón |
| author_facet | Pablo E. Layana Castro Antonio García Garví Antonio-José Sánchez-Salmerón |
| author_sort | Pablo E. Layana Castro |
| collection | DOAJ |
| description | Pose estimation of C. elegans in image sequences is challenging and even more difficult in low-resolution images. Problems range from occlusions, loss of worm identity, and overlaps to aggregations that are too complex or difficult to resolve, even for the human eye. Neural networks, on the other hand, have shown good results in both low-resolution and high-resolution images. However, training in a neural network model requires a very large and balanced dataset, which is sometimes impossible or too expensive to obtain.In this article, a novel method for predicting C. elegans poses in cases of multi-worm aggregation and aggregation with noise is proposed. To solve this problem we use an improved U-Net model capable of obtaining images of the next aggregated worm posture. This neural network model was trained/validated using a custom-generated dataset with a synthetic image simulator. Subsequently, tested with a dataset of real images. The results obtained were greater than 75% in precision and 0.65 with Intersection over Union (IoU) values. |
| first_indexed | 2024-04-09T15:19:57Z |
| format | Article |
| id | doaj.art-fe36918cbce34aa5b8c4435d5b69d32e |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-09T15:19:57Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-fe36918cbce34aa5b8c4435d5b69d32e2023-04-29T14:50:38ZengElsevierHeliyon2405-84402023-04-0194e14715Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequencesPablo E. Layana Castro0Antonio García Garví1Antonio-José Sánchez-Salmerón2Universitat Politècnica de Valéncia, Instituto de Automática e Informática Industrial, Camino de Vera S/n, Edificio 8G Acceso D, Valencia, 46022, Valencia, SpainUniversitat Politècnica de Valéncia, Instituto de Automática e Informática Industrial, Camino de Vera S/n, Edificio 8G Acceso D, Valencia, 46022, Valencia, SpainCorresponding author.; Universitat Politècnica de Valéncia, Instituto de Automática e Informática Industrial, Camino de Vera S/n, Edificio 8G Acceso D, Valencia, 46022, Valencia, SpainPose estimation of C. elegans in image sequences is challenging and even more difficult in low-resolution images. Problems range from occlusions, loss of worm identity, and overlaps to aggregations that are too complex or difficult to resolve, even for the human eye. Neural networks, on the other hand, have shown good results in both low-resolution and high-resolution images. However, training in a neural network model requires a very large and balanced dataset, which is sometimes impossible or too expensive to obtain.In this article, a novel method for predicting C. elegans poses in cases of multi-worm aggregation and aggregation with noise is proposed. To solve this problem we use an improved U-Net model capable of obtaining images of the next aggregated worm posture. This neural network model was trained/validated using a custom-generated dataset with a synthetic image simulator. Subsequently, tested with a dataset of real images. The results obtained were greater than 75% in precision and 0.65 with Intersection over Union (IoU) values.http://www.sciencedirect.com/science/article/pii/S2405844023019229Caenorhabditis elegansSkeletonizingSynthetic datasetLow-resolution imageU-Net |
| spellingShingle | Pablo E. Layana Castro Antonio García Garví Antonio-José Sánchez-Salmerón Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences Heliyon Caenorhabditis elegans Skeletonizing Synthetic dataset Low-resolution image U-Net |
| title | Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences |
| title_full | Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences |
| title_fullStr | Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences |
| title_full_unstemmed | Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences |
| title_short | Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences |
| title_sort | automatic segmentation of caenorhabditis elegans skeletons in worm aggregations using improved u net in low resolution image sequences |
| topic | Caenorhabditis elegans Skeletonizing Synthetic dataset Low-resolution image U-Net |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023019229 |
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