Publication
Segmentation of fetal 2D images with deep learning: a review
| dc.contributor.author | Rodrigues, Pedro João | |
| dc.contributor.author | Rehman, M. Anees ur | |
| dc.contributor.author | Igrejas, Getúlio | |
| dc.date.accessioned | 2024-01-22T09:06:30Z | |
| dc.date.available | 2024-01-22T09:06:30Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Image segmentation plays a vital role in providing sustainable medical care in this evolving biomedical image processing technology. Nowadays, it is considered one of the most important research directions in the computer vision field. Since the last decade, deep learning-based medical image processing has become a research hotspot due to its exceptional performance. In this paper, we present a review of different deep learning techniques used to segment fetal 2D images. First, we explain the basic ideas of each approach and then thoroughly investigate the methods used for the segmentation of fetal images. Secondly, the results and accuracy of different approaches are also discussed. The dataset details used for assessing the performance of the respective method are also documented. Based on the review studies, the challenges and future work are also pointed out at the end. As a result, it is shown that deep learning techniques are very effective in the segmentation of fetal 2D images. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Rodrigues, Pedro João; Rehman, M. Anees ur; Igrejas, Getúlio (2022). Segmentation of fetal 2D images with deep learning: a review. In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). p. 1-8. ISBN 978-1-6654-7095-7 | pt_PT |
| dc.identifier.doi | 10.1109/ICECCME55909.2022.9988625 | pt_PT |
| dc.identifier.isbn | 978-1-6654-7095-7 | |
| dc.identifier.uri | http://hdl.handle.net/10198/29275 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Image segmentation | pt_PT |
| dc.subject | Fetal images | pt_PT |
| dc.subject | Deep learning techniques | pt_PT |
| dc.subject | CNN | pt_PT |
| dc.title | Segmentation of fetal 2D images with deep learning: a review | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 8 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) | pt_PT |
| person.familyName | Rodrigues | |
| person.familyName | Igrejas | |
| person.givenName | Pedro João | |
| person.givenName | Getúlio | |
| person.identifier.ciencia-id | 1316-21BB-9015 | |
| person.identifier.orcid | 0000-0002-0555-2029 | |
| person.identifier.orcid | 0000-0002-6820-8858 | |
| person.identifier.rid | M-8571-2013 | |
| person.identifier.scopus-author-id | 47761255900 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 6c5911a6-b62b-4876-9def-60096b52383a | |
| relation.isAuthorOfPublication | ab4092ec-d1b1-4fe0-b65a-efba1310fd5a | |
| relation.isAuthorOfPublication.latestForDiscovery | 6c5911a6-b62b-4876-9def-60096b52383a |