DPC-MSGATNet: dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography

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Qiao, SiboThe School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, Shandong, China. (author)

DPC-MSGATNet : dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography

Article/chapterEnglish2023

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2023-01-17
Springer Nature,2023
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LIBRIS-ID:oai:DiVA.org:uu-500258
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-500258URI
https://doi.org/10.1007/s40747-023-00968-xDOI

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Language:English
Summary in:English

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Echocardiography is essential in evaluating fetal cardiac anatomical structures and functions when clinicians conduct early treatment and screening for congenital heart defects, a common and intricate fetal malformation. Nevertheless, the prenatal detection rate of fetal CHD remains low since the peculiarities of fetal cardiac structures and the variousness of fetal CHD. Precisely segmenting four cardiac chambers can assist clinicians in analyzing cardiac morphology and further facilitate CHD diagnosis. Hence, we design a dual-path chain multi-scale gated axial-transformer network (DPC-MSGATNet) that simultaneously models global dependencies and local visual cues for fetal ultrasound (US) four-chamber (FC) views and further accurately segments four chambers. Our DPC-MSGATNet includes a global and a local branch that simultaneously operates on an entire FC view and image patches to learn multi-scale representations. We design a plug-and-play module, Interactive dual-path chain gated axial-transformer (IDPCGAT), to enhance the interactions between global and local branches. In IDPCGAT, the multi-scale representations from the two branches can complement each other, capturing the same region’s salient features and suppressing feature responses to maintain only the activations associated with specific targets. Extensive experiments demonstrate that the DPC-MSGATNet exceeds seven state-of-the-art convolution- and transformer-based methods by a large margin in terms of F1 and IoU scores on our fetal FC view dataset, achieving a F1 score of 96.87% and an IoU score of 93.99%. The codes and datasets can be available at https://github.comQiaoSiBo/DPC-MSGATNet.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
Convolutional neural network
Echocardiography
Fetal four chambers
Semantic segmentation
Transformer

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Pang, ShanchenThe School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, Shandong, China. (author)
Luo, GangThe Heart Center, Qingdao Women and Children’s Hospital, Qingdao, 266034, Shandong, China. (author)
Sun, YiThe Heart Center, Qingdao Women and Children’s Hospital, Qingdao, 266034, Shandong, China. (author)
Yin, WenjinThe School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, Shandong, China. (author)
Pan, SilinThe Heart Center, Qingdao Women and Children’s Hospital, Qingdao, 266034, Shandong, China. (author)
Lv, Zhihan,Dr.1984-Uppsala universitet,Institutionen för speldesign(Swepub:uu)zhilv527 (author)
The School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, Shandong, China.The Heart Center, Qingdao Women and Children’s Hospital, Qingdao, 266034, Shandong, China. (creator_code:org_t)

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In:Complex & Intelligent Systems: Springer Nature:9, s. 4503-45192199-45362198-6053

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By the author/editor: Qiao, Sibo; Pang, Shanchen; Luo, Gang; Sun, Yi; Yin, Wenjin; Pan, Silin; show more...; Lv, Zhihan, Dr. ...; show less...

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