{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:20:05Z","timestamp":1760145605588,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have achieved remarkable success in fully supervised medical image segmentation tasks. However, the acquisition of large quantities of homogeneous labeled data is challenging, making semi-supervised training methods that rely on a small amount of labeled data and pseudo-labels increasingly popular in recent years. Most existing semi-supervised learning methods, however, underestimate the importance of the unlabeled regions during training. This paper posits that these regions may contain crucial information for minimizing the model\u2019s uncertainty prediction. To enhance the segmentation performance of the left-atrium database, this paper proposes a triple consistency segmentation network based on the squeeze-and-excitation mechanism (SETC-Net). Specifically, the paper constructs a symmetric architectural unit called SEConv, which adaptively recalibrates the feature responses in the channel direction by modeling the inter-channel correlations. This allows the network to adaptively weigh each channel according to the task\u2019s needs, thereby emphasizing or suppressing different feature channels. Moreover, SETC-Net is composed of an encoder and three slightly different decoders, which convert the prediction discrepancies among the three decoders into unsupervised loss through a constructed iterative pseudo-labeling scheme, thus encouraging consistent and low-entropy predictions. This allows the model to gradually capture generalized features from these challenging unmarked regions. We evaluated the proposed SETC-Net on the public left-atrium (LA) database. The proposed method achieved an excellent Dice score of 91.14% using only 20% of the labeled data. The experiments demonstrate that the proposed SETC-Net outperforms seven current semi-supervised methods in left-atrium segmentation and is one of the best semi-supervised segmentation methods on the LA database.<\/jats:p>","DOI":"10.3390\/sym16081041","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T05:23:27Z","timestamp":1723613007000},"page":"1041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semi-Supervised Left-Atrial Segmentation Based on Squeeze\u2013Excitation and Triple Consistency Training"],"prefix":"10.3390","volume":"16","author":[{"given":"Dongsheng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiezhen","family":"Xv","sequence":"additional","affiliation":[{"name":"School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0196-6758","authenticated-orcid":false,"given":"Jianshen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiehui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxi","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0429-5339","authenticated-orcid":false,"given":"Lijie","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China"},{"name":"Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province, Hebei University of Engineering, Handan 056038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.media.2019.04.005","article-title":"Abdominal multi-organ segmentation with organ-attention networks and statistical fusion","volume":"55","author":"Wang","year":"2019","journal-title":"Med. 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