MOTC: Abdominal Multi-objective Segmentation Model with Parallel Fusion of Global and Local Information.
Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional operations restricts the modeling of long-term dependencies. The introduction of Transformer enables the modeling of long-term dependencies and partia...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 1 - 17 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178678183&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678183 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678183 178678183 178678183 10.1007/s10278-024-00978-2 178678183 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MOTC: Abdominal Multi-objective Segmentation Model with Parallel Fusion of Global and Local Information. aug: au: Zhang, GuoDong Gu, WenWen Wang, SuRan Li, YanLin Zhao, DaZhe Liang, TingYu Gong, ZhaoXuan Ju, RongHui affil: https://ror.org/02423gm04 School of Computer, Shenyang Aerospace University, Daoyi South Street, 110135, Shenyang, Liaoning Province, China sug: subj: Radiography, Abdominal Image Processing, Computer Assisted Models, Statistical Human Descriptive Statistics Neural Networks (Computer) Image Interpretation, Computer Assisted Tomography, X-Ray Computed Health Informatics Algorithms Image Enhancement Digital Imaging ab: Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional operations restricts the modeling of long-term dependencies. The introduction of Transformer enables the modeling of long-term dependencies and partially eliminates the local inductive bias in convolutional operations, thereby improving the accuracy of tasks such as segmentation and classification. Researchers have proposed various hybrid structures combining Transformer and Convolutional Neural Networks. One strategy is to stack Transformer blocks and convolutional blocks to concentrate on eliminating the accumulated local bias of convolutional operations. Another strategy is to nest convolutional blocks and Transformer blocks to eliminate bias within each nested block. However, due to the granularity of bias elimination operations, these two strategies cannot fully exploit the potential of Transformer. In this paper, a parallel hybrid model is proposed for segmentation, which includes a Transformer branch and a Convolutional Neural Network branch in encoder. After parallel feature extraction, inter-layer information fusion and exchange of complementary information are performed between the two branches, simultaneously extracting local and global features while eliminating the local bias generated by convolutional operations within the current layer. A pure convolutional operation is used in decoder to obtain final segmentation results. To validate the impact of the granularity of bias elimination operations on the effectiveness of local bias elimination, the experiments in this paper were conducted on Flare21 dataset and Amos22 dataset. The average Dice coefficient reached 92.65% on Flare21 dataset, and 91.61% on Amos22 dataset, surpassing comparative methods. The experimental results demonstrate that smaller granularity of bias elimination operations leads to better performance. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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