Zhenyuan Ning , Yixiao Mao , Xiaotong Xu, Qianjin Feng, Shengzhou Zhong, Yu Zhang
{"title":"DC-Net:超声图像病变分割的显著性图分解与耦合","authors":"Zhenyuan Ning , Yixiao Mao , Xiaotong Xu, Qianjin Feng, Shengzhou Zhong, Yu Zhang","doi":"10.1016/j.engappai.2025.110355","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate lesion segmentation in ultrasound images faces the unique challenge that adjacent tissues (<em>i.e.</em>, background) share similar intensity and often exhibit richer texture patterns than the lesion region (<em>i.e.</em>, foreground). This work presents a decomposition-coupling network, called DC-Net, to deal with this challenge in a (foreground-background) saliency map disentanglement-fusion manner. The DC-Net consists of decomposition and coupling subnets, and the former preliminarily disentangles original image into foreground and background saliency maps, followed by the latter for accurate segmentation under the assistance of saliency prior fusion. The coupling subnet involves three aspects of fusion strategies, including: (1) regional feature aggregation (via differentiable context pooling operator in the encoder) to adaptively preserve local contextual details with the larger receptive field during dimension reduction; (2) relation-aware representation fusion (via cross-correlation fusion module in the decoder) to efficiently fuse low-level visual characteristics and high-level semantic features during resolution restoration; (3) dependency-aware prior incorporation (via coupler) to reinforce foreground-salient representation with the complementary information derived from background representation. Furthermore, a harmonic loss function is introduced to encourage the network to focus more attention on low-confidence and hard pixels. The proposed method is evaluated on two ultrasound lesion segmentation tasks, achieving better performance than second best methods (<em>i.e.</em>, dice similarity coefficient: 73.96 ± 0.36 (+4.18%)/76.25 ± 1.89 (+3.88%), mean intersection over union/jaccard index: 64.26 ± 0.48 (+5.07%)/65.28 ± 2.24 (+6.46%), precision: 75.82 ± 1.16 (+8.07%)/79.35 ± 1.92 (+3.07%), 95% hausdorff distance: 5.44 ± 0.10 (+5.39%)/6.22 ± 0.26 (+5.18%) on breast/thyroid ultrasound). Our proposed DC-Net can adapt to multiple ultrasound segmentation tasks and achieve remarkable performance improvement. Our code is available at <span><span>https://github.com/smu-myx/DC-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"148 ","pages":"Article 110355"},"PeriodicalIF":8.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DC-Net: Decomposing and coupling saliency map for lesion segmentation in ultrasound images\",\"authors\":\"Zhenyuan Ning , Yixiao Mao , Xiaotong Xu, Qianjin Feng, Shengzhou Zhong, Yu Zhang\",\"doi\":\"10.1016/j.engappai.2025.110355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate lesion segmentation in ultrasound images faces the unique challenge that adjacent tissues (<em>i.e.</em>, background) share similar intensity and often exhibit richer texture patterns than the lesion region (<em>i.e.</em>, foreground). This work presents a decomposition-coupling network, called DC-Net, to deal with this challenge in a (foreground-background) saliency map disentanglement-fusion manner. The DC-Net consists of decomposition and coupling subnets, and the former preliminarily disentangles original image into foreground and background saliency maps, followed by the latter for accurate segmentation under the assistance of saliency prior fusion. The coupling subnet involves three aspects of fusion strategies, including: (1) regional feature aggregation (via differentiable context pooling operator in the encoder) to adaptively preserve local contextual details with the larger receptive field during dimension reduction; (2) relation-aware representation fusion (via cross-correlation fusion module in the decoder) to efficiently fuse low-level visual characteristics and high-level semantic features during resolution restoration; (3) dependency-aware prior incorporation (via coupler) to reinforce foreground-salient representation with the complementary information derived from background representation. Furthermore, a harmonic loss function is introduced to encourage the network to focus more attention on low-confidence and hard pixels. The proposed method is evaluated on two ultrasound lesion segmentation tasks, achieving better performance than second best methods (<em>i.e.</em>, dice similarity coefficient: 73.96 ± 0.36 (+4.18%)/76.25 ± 1.89 (+3.88%), mean intersection over union/jaccard index: 64.26 ± 0.48 (+5.07%)/65.28 ± 2.24 (+6.46%), precision: 75.82 ± 1.16 (+8.07%)/79.35 ± 1.92 (+3.07%), 95% hausdorff distance: 5.44 ± 0.10 (+5.39%)/6.22 ± 0.26 (+5.18%) on breast/thyroid ultrasound). Our proposed DC-Net can adapt to multiple ultrasound segmentation tasks and achieve remarkable performance improvement. Our code is available at <span><span>https://github.com/smu-myx/DC-Net</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":\"148 \",\"pages\":\"Article 110355\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197625003550\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197625003550","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
DC-Net: Decomposing and coupling saliency map for lesion segmentation in ultrasound images
Accurate lesion segmentation in ultrasound images faces the unique challenge that adjacent tissues (i.e., background) share similar intensity and often exhibit richer texture patterns than the lesion region (i.e., foreground). This work presents a decomposition-coupling network, called DC-Net, to deal with this challenge in a (foreground-background) saliency map disentanglement-fusion manner. The DC-Net consists of decomposition and coupling subnets, and the former preliminarily disentangles original image into foreground and background saliency maps, followed by the latter for accurate segmentation under the assistance of saliency prior fusion. The coupling subnet involves three aspects of fusion strategies, including: (1) regional feature aggregation (via differentiable context pooling operator in the encoder) to adaptively preserve local contextual details with the larger receptive field during dimension reduction; (2) relation-aware representation fusion (via cross-correlation fusion module in the decoder) to efficiently fuse low-level visual characteristics and high-level semantic features during resolution restoration; (3) dependency-aware prior incorporation (via coupler) to reinforce foreground-salient representation with the complementary information derived from background representation. Furthermore, a harmonic loss function is introduced to encourage the network to focus more attention on low-confidence and hard pixels. The proposed method is evaluated on two ultrasound lesion segmentation tasks, achieving better performance than second best methods (i.e., dice similarity coefficient: 73.96 ± 0.36 (+4.18%)/76.25 ± 1.89 (+3.88%), mean intersection over union/jaccard index: 64.26 ± 0.48 (+5.07%)/65.28 ± 2.24 (+6.46%), precision: 75.82 ± 1.16 (+8.07%)/79.35 ± 1.92 (+3.07%), 95% hausdorff distance: 5.44 ± 0.10 (+5.39%)/6.22 ± 0.26 (+5.18%) on breast/thyroid ultrasound). Our proposed DC-Net can adapt to multiple ultrasound segmentation tasks and achieve remarkable performance improvement. Our code is available at https://github.com/smu-myx/DC-Net.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.